We have a few years before Nvidia will show the market it’s true earnings potential. When a thesis is not reflected in the revenue segments yet, there are typically lower entry points and ongoing volatility. You’ll see in the technical analysis that although I could not be more bullish on this stock long-term, there is weakness in the semiconductor sector and we hope this translates to a lower entry point for our readers.
The market is also in a fierce debate between AMD, Intel, and Nvidia and is also distracted by other chips, such as Micron and NXP. In my analysis, I look for growth. How big is the market relative to how big the company is now?
You can ignore Nvidia’s gaming revenue and other segments for the main trajectory that we are focused on. Gaming is great for stability and earnings reports, but the growth will not be from gaming (a market where Nvidia is already a mature, market leader). I’m also not focused on PC sales or the CPU-powered cloud, as the first is not a growth market and the second is not the piece in the cloud stack that will accelerate future technologies.
I’ve written at length about the Mellanox acquisition and it’s a great reference for Nvidia’s long-term strategy. In this report, I’d like to break down what the GPU-powered cloud is capable of and why it’s important to differentiate Nvidia’s strategy from the competitors (and some competitors to keep an eye on).
0e2c95a4-828b-4858-bbee-199b8f618cb4_Nvidia-Premium-Analysis-1.pdf
Nvidia Premium Analysis
Introduction:
I’ve covered Nvidia a few times on my free blog, however, it would be a disservice to my premium members to not formally initiate coverage and provide critical updates to the GPU-powered cloud and AI economy as it’s built out. I believe Nvidia will be on my short list in a decade from now.
To be bold – I believe Nvidia will be one of the world’s most valuable companies by 2030. The research below organizes my investment thesis for the GPU-powered cloud and why I believe Nvidia will emerge as a clear leader.
The question is how long can you have your money in a stock? This is a long-term play that requires a 10-year hold for the full return. Like Amazon, Google and Netflix, it requires ten years before emerging technology goes from a moonshot, to a viable company, to a less volatile company – and finally, to a profitable machine. Nvidia is a viable company that is volatile, proven during the crypto surge. Investors should see the crypto bust as an opportunity as the issues were not based on Nvidia’s core business. The trade war certainly hasn’t helped the stock price, either.
We have a few years before Nvidia will show the market it’s true earnings potential. When a thesis is not reflected in the revenue segments yet, there are typically lower entry points and ongoing volatility. You’ll see in the technical analysis that although I could not be more bullish on this stock long-term, there is weakness in the semiconductor sector and we hope this translates to a lower entry point for our readers.
The market is also in a fierce debate between AMD, Intel, and Nvidia and is also distracted by other chips, such as Micron and NXP. In my analysis, I look for growth. How big is the market relative to how big the company is now?
You can ignore Nvidia’s gaming revenue and other segments for the main trajectory that we are focused on. Gaming is great for stability and earnings reports, but the growth will not be from gaming (a market where Nvidia is already a mature, market leader). I’m also not focused on PC sales or the CPU-powered cloud, as the first is not a growth market and the second is not the piece in the cloud stack that will accelerate future technologies.
I’ve written at length about the Mellanox acquisition and it’s a great reference for Nvidia’s long-term strategy. In this report, I’d like to break down what the GPU-powered cloud is capable of and why it’s important to differentiate Nvidia’s strategy from the competitors (and some competitors to keep an eye on).
SECTION 1: Artificial Intelligence
Artificial intelligence is a collection of categories, including computer vision, natural language, virtual assistants, robotic process automation and advanced machine learning. The AI impact will not be linear, rather adoption will resemble an S-curve pattern with a slow start due to the substantial costs and investment required for applications. The slow start will be followed by an acceleration that is driven by competition across capabilities and innovations. The penalty is steep for laggards, as pointed out below.
The slow start to AI will cause many investors to become complacent, not realizing the artificial intelligence boat will quickly leave the shore, metaphorically speaking, once it is closed to new entrants. If you add in the inevitable recession that will follow this long bull market, we could see many investors rotate back to growth stocks too late to realize the full gains from AI.
As noted in the graph below, we should see an AI acceleration around 2022-2023. The last call for decent gains in AI will be in 2025, although by then, the gains will be somewhat diminished compared to investors who choose their AI stocks by 2021/2022.
Basically, weigh the costs adding artificial intelligence to your portfolio earlier as opposed to later in the tech cycle as it’ll be one of the biggest economic growth drivers in history (more on this below).
source: McKinsey
Nvidia’s acceleration may happen one or two years earlier as they are the core piece in the stack that is required for the computing power for the front-runners referenced in the graph above. There is a chance Nvidia reflects data center growth as soon as 2020-2021.
Between the years 2025 and 2030, the stage after AI infancy, artificial intelligence is expected to add $13-$15 trillion to global economic activity, or 1.2 percent additional GDP growth per year. Compare this to the spread of information technology (IT) in the 2000s, which added 0.6 percent. Also, compare this to 5G technologies, which are expected to add $2.2 trillion over the next 15 years.
McKinsey points out that front-runners, who are currently investing in artificial intelligence, will reflect an increase in positive cash flow of up to 120% from their AI investments. The capital required to invest in AI, however, is negatively affecting cash flow right now and will continue to do so throughout the next year or two. Laggards are expected to lose at least 20% of their cash flow from the negative impact of investing in AI too late (or not at all).
Most investors today are well aware of what mobile and cloud did for tech stocks. These gains will seem minor in comparison to what artificial intelligence will do for your portfolio by choosing the right companies.
Today, Nvidia is my top pick for AI. I will also be providing more AI stock picks throughout the next 1-3 years to ensure my readers are well prepared for the massive gains AI will deliver by 2030. I believe this report is being delivered before at least one more pullback in Nvidia’s future due to broader semiconductor weakness.
It is too early for the data center to make an impact and this trajectory (data center) is what we are targeting. When I deliver information well before momentum, it helps to be patient with an entry and one of the main benefits of my reports is to give you that time, when possible.
SECTION 2: Competitive Positioning
Desktop GPUs is not the growth category that I am targeting for this investment thesis, which is why many oftcited statistics are irrelevant. For example:
“AMD shipments increased 9.8%, Nvidia was flat and Intel's shipments, decreased -1.4% as indicated in the following chart.” -John Peddie Research
Many financial analysts and authors on Seeking Alpha (etcetera) are quick to think this means AMD is the better investment, whereas this statistic refers to a mature market. To cut through the noise, it’s important to remember this thesis is about the GPU-powered cloud.
I’ve already covered why AMD is not as big of a threat to Nvidia as Wall Street believes. AMD has its hands full competing with Intel on the CPU-powered cloud and does not have the CUDA programming platform (more on this below). The two are competing in other segments (gaming, PCs) but those are less of a concern to the buyand-hold thesis in this report (data center). I can’t stress enough to separate these segments if Nvidia is of interest to you as a growth story.
Competitors to watch for at this layer in the data center stack are:
• Xilinx’s FPGAs,
• Intel’s FPGAs (through the acquisition with Alterra),
• Google’s TPUs (essentially an ASIC on the efficiency/flexibility spectrum).
FPGAs have distinct advantages over GPUs as they offer a higher amount of on-chip cache memory to help reduce the bottlenecks from external memory, and are flexible enough to be reconfigured for various data types, such as binary, ternary, and custom data types, whereas GPUs must be modified at the vendor level.
FPGAs are also known for power efficiency and test at 10x better in power consumption than GPUs and also 4x better than GPUs for general purpose compute. Reconfigurability for FPGAs help provide efficiency beyond deep learning for a large number of end applications and workloads.
The architecture of FPGAs are very adaptable as the chips allow a user to address all of the needs of a workload with the resources provided by FPGAs. Meanwhile, GPUs are restricted as the architecture is a Single Instruction Multiple Thread (SIMT), which provides an advantage over CPUs but can result in lower performance efficiency.
Today, FPGAs require knowledge of machine learning algorithms at the hardware level, in addition to the software development, and this is the barrier to entry for FPGAs.
As readers of mine know, I like Xilinx and this will be a stock I cover in the future with a full-length report. The company operates in a niche, is the inventor of FPGAs and has the ability to attract developers to its ecosystem. The challenge with FPGAs is they are hard to program as most software developers are not able to program hardware. Xilinx is working on becoming more of a platform company to solve this issue, and if the company succeeds, it’ll be a worthwhile investment.
Intel will face headwinds with developers, who are the ultimate decision makers for any ecosystem. Even now, you will be hard pressed to hear much discussion on developer forums and news feeds about Intel/Alterra’s FPGAs. Developers tend to avoid overly-corporate companies and cultures, and Xilinx has a serious shot of overcoming Intel if they execute correctly.
AMD also has a decent chance of eating away at Intel’s market share on the CPU-powered cloud. Overall, I prefer pure play options, when possible, and most of my tech stock coverage focuses on this. In my opinion, Intel is not the growth story in these categories.
This brings us to Google’s TPUs. TensorFlow is rising in popularity as a machine learning language and TPUs primarily run TensorFlow models. This is one of Google’s more successful experiments. They are cheaper and use less power than GPUs and are specifically focused on machine learning.
TPUs train and run machine learning models and power Google Translate, Photos, Search, Assistant and Gmail – i.e. image recognition, language translation, speech recognition and image generation. TPUs do not compete with GPUs in other areas of artificial intelligence.
It’s also important to remember that Nvidia and Xilinx are hardware companies that offer platforms for software developers. This is a distinct advantage compared to software companies (Apple, Google and Facebook) trying to release hardware chips. The market is so valuable, that they will most certainly try, but I think there are a lot of technical hurdles for a software company competing in the chip space other than Google. Workday’s cloud financial management solutions have less traction with 8 companies in the Fortune 500 and 530 customers overall.
SECTION 3: Developer Ecosystem
In November of 2018, I wrote about Nvidia’s developer ecosystem as a primary moat. GPUs are hardware which require software to write applications and utilize GPUs. Nvidia has a special language called CUDA that is universally known due to a first mover advantage in GPUs.
This ecosystem is not apparent to the public markets right now because new technology is developed in waves, and funded by venture capitalists in cycles. We are seeing the last of the mobile era of venture-funded companies with the IPOs of Uber and Lyft, — which began with Twitter, Yelp, Spotify — and was also reflected in Facebook’s epic rise from mobile native app revenue.
We are in the later stages of the venture-funded cycle for cloud software, hence a string of newly public companies over the last two to three years with some runway to go in this category before the majority of use cases are claimed. For artificial intelligence, it is so early that it’s essentially invisible right now to the public markets as development teams are beginning to form.
The strength of the developer ecosystem is what propelled Apple to become a $1 trillion company. While many investors look at iPhone sales, and Mac sales, the ecosystem that created by application developers is why Apple had an impenetrable moat. If the iPhone only had applications from Apple on the device (iTunes, iOS Maps, Safari browser), then many device manufacturers could have competed with Apple. The moat that Apple has enjoyed was created by the third-party developers who created iPhone applications in C and C++ with XCode, which made the device more attractive due to the mobile app ecosystem.
Android then became the second operating system in the mobile duopoly. Due to the friction of learning too many languages, the mobile ecosystem did not entertain any further competitors. This is despite there being 5 billion smartphones globally (i.e. it’s certainly feasible from a consumer supply/demand view point to entertain more operating systems and app stores), yet the limitation came from the number of languages developers are willing to learn. Microsoft Windows failed because it launched too late, and developers had already chosen the two languages they were willing to work with.
This is what is meant by developer ecosystem. Devices themselves do not have moats. The developer ecosystem creates the moat as third-party developers favor developing on certain operating systems and there is a limit to the programming languages they will learn before it impedes progress for the developer and the company the developer works for.
This is what is happening with Nvidia’s CUDA. The chips themselves do not create the moat. The compute platform creates the moat. Due to the need for a universal language to build GPU-accelerated applications, universities are teaching CUDA, and students are graduating knowing Tesla/Volta chips over competing chips, such as AMD’s Radeon or FPGAs or TPUs.
Here’s a quote from Marc Andreessen of Andreessen-Horowitz, one of the most successful venture capitalists in Silicon Valley: “We’ve been investing in a lot of startups applying deep learning to many areas, and every single one effectively comes in building on Nvidia’s platform. It’s like when people were all building on Windows in the ’90s or all building on the iPhone in the late 2000s.”
Here's another quote from a developer on Reddit:
“Nvidia, thanks to the CUDA software stack (which AMD cannot match), has a much more unassailable position than does Intel with Xeon CPUs (where an X86 application just runs on either a Xeon or an Epyc).”
– software developer on Reddit
SECTION 4: Financials
In this case, I began with the investment thesis rather than the financials as the two do not sync up today. Gaming is Nvidia’s strongest revenue segment with $1.3 billion per quarter. Data center revenue has been flat to declining for three straight quarters, ranging between $634 million and $679 million.
The market is encouraged, yet cautious, with revenue of $2.58 billion in the most recent quarter fiscal Q2 2020, up 16.2%, yet down about 17% from $3.12 billion in the year-ago quarter.
GAAP earnings was $0.90, compared to $1.76 a year ago, and $0.64 in the previous quarter. Non-GAAP earnings of $1.24 in the current quarter, compared to $1.94 a year earlier, and $0.88 in the previous quarter.
Similarly, net income was down 50% year-over-year but up 40% quarter-over-quarter at $552 million.
Next quarter, Nvidia is expecting $2.90 billion, plus or minus 2 percent, with gross margins of 62%.
The bigger story this quarter was Nvidia’s gaming growth, which reflected 24% growth sequentially, with revenue of 1.31 billion compared to estimates of 1.29 billion. There is also evidence that inventory is normalizing with an inventory ratio of 50% and on track to lower to around 40% in fiscal Q3 compared to the previous ratio of 71%.
As stated, the current financials do not reflect the growth expected from AI.
SECTION 5: Technical Analysis
By Knox Ridley
5.1 Trend Lines and Internal Strength
Focusing on the black trend lines, you’ll see three sets. The upward trend in price, which coincides with the upward trend in momentum in the RSI, tracks two recent topping patterns in Nvidia’s price action.
The trendlines coincide with each other. When both momentum and price roughly trend together, it can show a healthy trend in place. Using these corresponding trends can also offer reasonable warnings, as well.
By following the approximate time at which both the RSI trend and price trend broke to the downside together, we can see safe and effective exits that allow you to side-step pull backs.
Further information can be found by looking at the set of green and red arrows. Notice that just before the last top in Nvidia, before the May correction, as the price of Nvidia climbed higher, the momentum in the RSI was decreasing. So, when I see a divergence like this coupled with trendlines being violated, it’s a warning of a correction on the horizon.
This exact pattern is unfolding today if we look at the last set of trend lines in the price and RSI. With Nvidia’s RSI closing just below oversold levels and pointing down towards the black trendline, this same negative divergence pattern is unfolding in real time, signaling a weakening of momentum. A break in this trend on both the RSI and price, and we can expect more downside to follow.
5.2 Elliot Wave Analysis
If we are forming a double top pattern, and Nvidia fails to break out above the $200 barrier, we can look at the retrace levels and expected extensions to gauge the likely targets for entry. First, if we zoom into the internal structure of Nvidia’s 3-month drop, which is highlighted by the light blue roman numeral count, you’ll see a very clear 5-wave drop.
It’s hard to see anything other than 5-waves in this move, all of which line up with internal Fibonacci ratios. The first pattern in a correction being a 5-wave drop is a strong indication of a 5-3-5 corrective pattern, which is highlighted in purple.
So, the Purple A was a 5-wave drop, while the purple B was a 3-wave correction, which touched the 50% retrace of Wave A before falling. If this count is correct, we are just now completing the 2nd wave in the final 5-wave pattern.
It’s always worth noting how the stock price reacts to these extensions. The stock found major support at the 61.8% retrace, testing this level before meeting heavy resistance at the 38.2% retrace. If Nvidia cannot break through the 38.2% retrace, the analysis is suggesting that we could see the final C wave play out, which will have Nvidia retest the 61.8% retrace level and likely make new lows.
However, I want to be clear with our convictions in this position. Anywhere between $125 to $160 is a great entry. Even though the analysis in this count is suggesting that we could see lower lows, and correction will be welcomed for a long-term entry. Thus, we will update any entries if the pull-back scenario takes hold.
Due to Nvidia’s fundamental strength, we will now look outside the individual stock for the cause of a sentiment shift. Please also refer to the bullish scenario below.
5.3 Global Semiconductor Sector
Like most semiconductors, Nvidia is a global company that is manufactured overseas (mainly from Taiwan) with 44% of its sales coming from China. Semiconductors are cyclical and sensitive to economic cycles. This is why we must look holistically at this sector when guiding an entry.
The Korean KOSPI Index
South Korea is an economy that is fueled by some of the world’s largest semi-conductor companies, as well as many mid-level players. Companies such as Samsung, and SK Hynix supplied over 60% of the components used in memory chips sold globally in 2018. So, the KOSPI can provide more information about the global health of semiconductors.
Since 2011, the index has been in a long-term uptrend, which it respected until very recently.
The KOSPI broke through this trendline, highlighted in black, which coincides with the 61.8% retrace. This level is now acting as resistance as the KOSPI is showing a negative RSI reversal pattern, which is indicating more downside is likely to follow.
This pattern is highlighted by the blue circles. In short, as the price makes lower highs, the RSI is making higher highs, indicating that the buying pressure is not sufficient to reverse the trend as it reaches oversold levels. This chart is anything but encouraging.
Philadelphia Semiconductor (PHLX)
The Philadelphia Semiconductor index (PHLX) will have some international exposure, such as NXP Semiconductors and Taiwan Semiconductor Manufacturing Company, but it is populated with mostly US companies. Nvidia accounts for nearly 9% of its total value.
Looking at the weekly chart, we can get a glimpse of the bigger patterns at play, which I believe are pertinent for where we are. If we start with the first long term trend from 2013 – 2015, you’ll notice that the price respected the first long-term trend line in magenta.
The black arrows indicate the moment when the RSI and price broke their respective trends. This trend broke first on the RSI, then fell to a lower long-term trend highlighted in black. Once this trend broke along with the black trend in the RSI, the index gave way to a significant correction. When these long-term trends break together, both in momentum and in price, it’s a warning to step aside.
Further evidence can be found by the descending red line in the RSI leading up to the drop. There is a negative divergence with the price and momentum. As the price action increased leading up to the sell-off in 2015, the RSI made significantly lower highs, failing to break out multiple times. When you see negative divergence that is followed by the RSI and price trend lines breaking, this is where technical analysis can help you avoid downside risk.
Today, the price has managed to find a bottom, and has resumed a new trend, which is also highlighted in black, and appears to be trading in a diagonal pattern. The RSI is showing a divergence between the RSI, making lower highs while the price makes higher highs, just like we saw leading up to the 2015 correction. Furthermore, just like in the prior run-up before the correction, the RSI’s momentum keeps failing to break out of the descending trend highlighted in red.
Taking what we are seeing today, the evidence is leaning towards a more cautious stance. The intermarket divergence we are seeing between the KOSPI and the PHLX, coupled with the weakness we are currently seeing in the PHLX, leads me to conclude that the KOSPI is a possible leading indicator of the semiconductor sector.
Nvidia, being a major global player in the semiconductor space, is not immune to this broad market weakness. With the weakness we are seeing with Nvidia’s chart as well, I think letting these patterns play out, will allow a safer and more optimal entry price.
5.4 The Bullish Scenario
I attempt to approach each chart from a blank slate. I let the data and analysis lead me with as little bias as possible. However, especially in a late cycle bull market, I always ask myself where I could be wrong, and what it would take for me to invalidate my primary thesis. The below chart is my alternative invalidation thesis.
From an Elliot Wave Count, the primary Wave 4 correction, which is highlighted in yellow, unfolded in an A-B-C pattern, highlighted in purple, and ended at the December low. This would mean that we are currently in the final 5th Wave push of the larger yellow count. This final 5th Wave will be an impulsive move, so its structure will have its own 5 waves. This means that we have completed the Wave 1 and Wave 2, highlighted in purple and are currently in Wave 3. If we look one degree lower into Wave 3, we have completed waves 1 and 2 of 3, and are about to breakout to the upside.
I have some issues with this count. Specifically, when you zoom into the lower degree structure, we can see a 5wave structure down, who’s ratios line up like we want to see, and we can also see 3-waves up. However, if we see a break above $200, this strongly implies that the Wave 4 in yellow is over, and that the bull trend can continue.
I will need to see the RSI divergence forming to be invalidated by the RSI breaking through the red trend line as well as the price of Nvidia break through the $200 level. At $200, we may have left some minor gains on the table, but it is a much safer entry than where the stock is trading right now and worth the insurance.