NVIDIA: How could alternative data be used to assess its long-term potential?
Discover how alternative data can reveal insights on NVIDIA's future, paving the way for smarter investment strategies and unearthing fresh insights in the GPU market.
Welcome to the Data Score newsletter, your go-to source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of alternative data insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from data. Before that, I successfully built a sellside equity research franchise based on proprietary data and non-consensus insights. Through my extensive experience as a purchaser and creator of data, I have gained a unique perspective that allows me to collaborate with end-users to generate meaningful insights.
In the intricate world of financial investment, predicting a company's long-term potential is a critical but challenging task. This article aims to help both financial professionals and data professionals think through how alternative data could be used to answer big uncertainties in the market.
I am using NVIDIA as a case study and will dive into how alternative data, beyond traditional financial metrics, can generate insights on key aspects: NVIDIA's GPU1 market presence, the evolving applications of GPUs, potential competitive threats, and the balancing act of supply chain investments. Unleashing the power of unique data sources, this article aims to inspire novel ways to evaluate a tech giant like NVIDIA, paving the way for more informed investment decisions.
This is meant to be the start of a conversation. I invite investment professionals to comment on the key investment debates facing NVIDIA and data professionals to suggest novel solutions that can provide an edge in our understanding. Let's leverage the collective creativity of the alternative data community to expand and innovate beyond the ideas presented here.
NVIDIA’s current situation
NVIDIA, the leading graphics processing unit (GPU) company, recently reached $1 trillion in market cap “as investors piled into the chipmaker that has quickly become one of the biggest winners of the AI boom. The stock's value has tripled in less than eight months, reflecting the surge in interest in artificial intelligence following rapid advances in generative AI” according to this Reuters article: https://www.reuters.com/technology/nvidia-sets-eye-1-trillion-market-value-2023-05-30/
The challenge for investors is that at this level, the share price implies the market expects very high sales growth, earnings growth, and/or improving ROIC2. Consensus3 earnings per share growth forecast by 35 sell analysts analysts averages 157% y/y in 2024 according to Nasdaq.com (https://www.nasdaq.com/market-activity/stocks/nvda), and grow another 39% in 2025, which places its current share price at ~47x in 2025 earnings per share. By contrast, the S&P 500 has a forward P/E ratio4 of around 18.5x (based on a collection of freely sourced estimates on the web found by Google Search on 13 June 2023).
NVIDIA Consensus Earnings Per Share:
NVIDIA P/E and PEG ratios based on consensus estimates:
My view on the key investment debates facing investment decision makers about NVIDIA
Alternative data is more granular than publicly reported results, which allows for a deeper assessment of the long-term answer than simply waiting for management to report results and update guidance. With each data-driven answer to the questions above, we adjust the probability of achieving the view being tested.
This isn’t an investment research report. So, I won’t take a view on the answers to these questions or share what the answers would mean for an investment decision. However, instead, I explain how I would approach the problem of assessing NVIDIA’s future fundamentals using alternative data.
Here’s my take on the big questions that investors would want to answer to assess if there is upside or downside to the current share price:
Will the mass adoption of generative AI support the expected high demand for GPU chips over the next 5 years to meet or beat consensus revenue estimates for NVIDIA’s data center division?
Can other use cases for GPU chips support additional growth (gaming, crypto, auto, VR, etc) over the next 5 years to meet or beat overall revenue expectations?
Will other competitors enter the market with credible products over the next 5 years to disrupt NVIDIA’s potential profitability as estimated by consensus?
How much will NVIDIA need to invest in production and supply chains to meet demand expectations over the next 5 years to meet or beat consensus cash flow and ROIC expectations?
These are long-term investment questions that reflect the nature of the growth expectations implied by the current share price. When selecting the questions to be answered, it's important to have a time frame and a benchmark level, here generically set as consensus expectations 5 years from now. I’m not taking a view on the answer, but it's better to be more precise with your own specific view and then test with data if it's achievable or not.
Even though these are 5-year time horizon questions, there are 20 short-term quarters of results in a row to get there. These act as the market’s near-term data points to assess if the long-term is possible. Alternative data is more granular than publicly reported results, which allows for a deeper assessment of the long-term answer than simply waiting for management to report results and update guidance. With each data-driven answer to the questions above, we adjust the probability of achieving the view being tested.
In other words, I believe both long-term and short-term investors should care about these critical questions.
Fellow investment professionals: What other questions would need to be answered? Are there more important questions to answer to be on the right side of the investment?
Brainstorming alternative data solutions to build the mosaic
With those bigger picture questions in mind, here are the datasets and applications that I would want to access and monitor.
1. Will the mass adoption of generative AI support the expected high demand for GPU chips over the next 5 years to meet or beat consensus revenue estimates for NVIDIA’s data center division?
What I would look for in the answer: disruptive change and accelerated growth stories require a series of sigmoid curves (S-curves)5 to play out over an extended period of time. Growth will eventually flatten out. But if growth flattens sooner than the market expects and is baked into the current share price valuation, then actions would need to be taken by companies to restimulate growth or risk missing long-term revenue growth. My view is that I would want multiple lenses on this question using different techniques and sources because each approach has its own bias and noise. I would also like to see trends by different geographies and industries because each will have its own adoption S-curve. Imagine a red-yellow-green-light system for each of these metrics. If growth stops expanding exponentially, put up the red light to stop and assess what’s going on and if long-term expectations are appropriate.