When it comes to writing content, three essential factors come into play: "perplexity," "burstiness," and "predictability." Perplexity refers to the complexity of the text, while burstiness measures the variations in sentence structure. On the other hand, predictability determines the likelihood of anticipating the next sentence. Human writers frequently mix longer, more complicated sentences with shorter ones, whereas AI-generated sentences frequently lack this variety. Therefore, for the content I am about to request, it's crucial to maintain a good amount of perplexity and burstiness, while minimizing predictability. Furthermore, please ensure that the content is written exclusively in English. Now, let's proceed with the rewrite:
To kick off this post, I must confess a couple of things. First and foremost, my investment portfolio has fared exceptionally well this year, even in a market that has been dominated by top-heavy tech companies. One major contributing factor is my ownership of shares in both NVIDIA and Microsoft—two companies that have greatly benefited from the AI revolution. Secondly, I must admit that my decision to invest in these stocks had nothing to do with foresight or forward thinking. In fact, the concept of AI wasn't even on my radar when I purchased shares in Microsoft back in 2014 and NVIDIA in 2018. It was merely a stroke of luck that placed me in the right place at the right time, reminding me that luck often outshines intelligence, at least in the realm of financial markets. That being said, the meteoric rise of NVIDIA's stock price has presented me with a dilemma—should I cash out or let my investment ride? This predicament necessitates an evaluation of how the promise of AI plays out in the company's valuation. Along the way, I will delve into the potential of AI and its perils for investors, drawing insights from lessons learned in the past.
The Semiconductor Industry
The semiconductor industry, as we know it today, experienced a significant growth spurt as a direct result of the PC revolution in the 1980s. As personal computers transitioned from niche tools to everyday work instruments for the masses, the demand for computer chips skyrocketed. Over the past four decades, these chips have become integral to almost every aspect of our lives, from household appliances to automobiles. Consequently, the companies involved in chip manufacturing have witnessed substantial increases in their fortunes, as well as occasional risks, driven by technological advancements.
1. Transitioning from High Growth to Maturity!
The semiconductor industry owes its initial boost to the personal computer business in the 1980s. As technology continued to permeate every facet of life, the industry experienced significant growth. To illustrate this growth, let's examine the aggregated revenues of global semiconductor companies from 1987 to 2023 (up to the first quarter) in the chart below:
Source: Semiconductor Industry Association
Starting from a negligible figure in the early 1980s, semiconductor company revenues soared during the 1980s and 1990s. Initially propelled by the PC business and later fueled by the dot-com boom, revenue growth for semiconductor companies slowed to single digits from 2001 to 2020. This was primarily due to a combination of increased chip demand in new applications and diminishing pricing power, leading to declining chip prices. Although revenue growth has regained momentum in the past three years, the industry has reached a state of maturity.
2. Consistent Profitability with Cyclical Patterns!
Throughout its existence, the semiconductor industry has generally been profitable, as indicated by the aggregate margins of companies operating in this sector:
While both gross and operating margins have consistently remained healthy, the recent increase in these metrics since 2010 is a testament to the higher profitability in certain segments of the chip business. However, it is important to note that profitability in the industry experiences cycles, as indicated by periodic dips in profitability over time, even as the business matures and faces increasing competition.
It is worth highlighting that these margins are actually understated due to the accounting treatment of research and development (R&D) expenses as operating expenses rather than capital expenditures. When adjusting for R&D, the operating margins of semiconductor companies are higher by approximately 2-4% in each time period. This adjustment involves adding back the R&D expense from the year and subtracting the amortization of R&D expenses over the prior five years, using straight-line amortization.
3. A Love-Hate Relationship with the Market!
As the semiconductor industry has expanded its revenues and profitability, investors have taken these operating results into account when determining the market capitalization of these companies. The following graph illustrates the combined enterprise value and market capitalization of global semiconductor companies in US dollars.
As observed, semiconductor companies have experienced prolonged periods of success, punctuated by challenging times in the market. The 1990s witnessed a decade of soaring market capitalizations, followed by a stagnant period from 2000 to 2010, with little growth in market capitalization. However, from 2011 to 2020, there was another decade of growth, resulting in a six-fold increase in market capitalizations. It is worth noting that semiconductor companies typically carry minimal debt burdens, resulting in enterprise values that sometimes fall below market capitalization in certain years. This is due to the presence of excess cash compared to debt. to debt) or closely align with market capitalization (when net debt is close to zero).
As market capitalizations have fluctuated over time, the valuation multiples of semiconductor companies relative to their revenues have also varied. The dot-com era saw semiconductor companies collectively trading at over seven times their revenues, followed by a long period where the valuation multiples ranged between two and three times revenues. However, between 2019 and 2021, the multiples spiked once again. These pricing trends seem to indicate that investors accept the maturity of the semiconductor business, as prices reflect market expectations for the future.
4. Ever-Changing Lineup: Winners and Losers Take the Stage!
As the semiconductor industry has matured, significant shifts have occurred in terms of the key players in the business and the major customers for semiconductor products. The following table showcases the transformation of the top ten semiconductor companies in terms of revenue, spanning from 1990 to 2023 at ten-year intervals:
Over time, the lineup of companies has undergone substantial changes, with only two firms, Intel and Texas Instruments, retaining their positions from the 1990 list to the 2023 list. Japanese companies have slipped or disappeared from the list, making room for Korean and Taiwanese counterparts. Notably, Taiwan Semiconductors has experienced the most remarkable advancement, claiming the top position in 2022. After enjoying a prolonged period at the top, Intel has descended to the third position in terms of revenues in 2022. It's worth mentioning that NVIDIA, the focal point of this post, held the eighth position on the 2023 list and has consistently maintained that rank since 2010. Although this may seem contradictory to its rising market capitalization, it exemplifies the company's strategic focus on pursuing niche markets with high profitability rather than striving for arbitrary growth.
The landscape of semiconductor chip customers has also evolved over time, with a shift away from personal computers towards smartphones. In the past decade, demand has emerged from sectors such as automotive, cryptocurrency, and gaming. Moreover, data processing has emerged as a significant driver of demand in recent years, indicating that an increasing portion of the global economy relies on computer chips.
The forecasts for the future, specifically in 2030, anticipated faster growth in the automobile and industrial electronics sectors. However, the potential surge in demand from AI products was largely underestimated, highlighting how quickly market forecasts can be overshadowed by real-world changes.
NVIDIA: Seizing Opportunities!
NVIDIA, founded by Jensen Huang in 1993, initially operated as a niche player in the semiconductor industry. Its significant growth occurred in the past decade, coinciding with the leveling off of revenues in the overall semiconductor business. In this section, we will delve into the company's history to uncover the factors behind its success and current position.
1. Growth through Opportunism and Profitability
NVIDIA went public on January 22, 1999, during the height of the dot-com boom, experiencing a remarkable 64% increase in its stock price on the offering date. At the time of its initial public offering, the company was profitable but generated relatively modest revenues of $160 million, positioning it as a minor player in the industry. As depicted in the graph below, its revenues grew steadily from 2000 to 2005, reaching $2.4 billion in 2005. Throughout the subsequent decade (2006-2015), the annual revenue growth rate moderated to 7-8% per year. Nevertheless, this growth propelled NVIDIA to secure a place among the top ten semiconductor companies by 2010. Timely investments in gaming and cryptocurrency fueled a surge in revenue growth to 27.19% between 2016 and 2020, a trend that has continued over the past two years.
The history of NVIDIA reveals two noteworthy aspects. Firstly, the company has sustained impressive growth despite the industry experiencing a slowdown in revenue growth (3.97% between 2011 and 2020). Secondly, this high revenue growth has been accompanied by not only profits but also above-average profitability, with NVIDIA's gross and operating margins surpassing industry averages. NVIDIA has effectively adopted a strategy of seizing opportunities and targeting growth markets within the chip industry, which has yielded remarkable success. Therefore, its current dominant position in the AI chip business serves as further evidence of the effectiveness of this strategic approach.
2. Substantial and Productive Reinvestment
In addition to NVIDIA's impressive growth and profitability, it is essential to consider the significant investments the company has made to drive this growth. As a semiconductor company, these reinvestments encompass not only expanding manufacturing capacity but also conducting research and development (R&D) to create the next generation of powerful and capable chips. Similar to the industry standard, I capitalized R&D at NVIDIA, applying a 5-year life and recalculating the operating income (as the reported version incorrectly treats R&D as an operating expense). This approach provides a corrected version of NVIDIA's pre-tax operating margin, which stands at 37.83%, and a pre-tax return on capital of 24.42% for the period of 2021-2023:
These figures exemplify NVIDIA's commitment to continuous innovation and investment in the development of cutting-edge chip technology. By capitalizing on growth markets within the chip business, the company's strategy has yielded remarkable results. Moreover, NVIDIA's dominant position in the AI chip sector serves as further testament to the effectiveness of this approach.
There is one final aspect of NVIDIA's business model that deserves attention, both in terms of profitability and risk. NVIDIA's core focus lies in research and chip design, rather than chip manufacturing. As a result, the company outsources the majority of its chip production to TSMC. NVIDIA's margins are derived from its ability to mark up the prices of these chips. However, it is important to acknowledge the potential risks associated with any future tensions between China and Taiwan, as such geopolitical factors could disrupt NVIDIA's supply chain.
3. Achieving a Mega Market Payoff
NVIDIA's exceptional performance in terms of operations has garnered significant recognition from the financial markets, as evidenced by its remarkable increase in market capitalization from its initial public offering (IPO) to reaching a trillion-dollar valuation. The graph below illustrates this remarkable ascent:
This substantial growth in market capitalization highlights the market's acknowledgment of NVIDIA's achievements and its position as a leading player in the industry. It serves as a testament to the company's success in capitalizing on growth opportunities and delivering substantial value to its shareholders.
I understand that many individuals now regret not owning NVIDIA throughout its impressive journey, but it's important to recognize that the company's path to success wasn't without its challenges. In fact, NVIDIA experienced near-death experiences in terms of market value in both 2002 and 2008, enduring losses of over 80% in market value. Personally, I attribute my fortunate investment in NVIDIA to one of those downturns in 2018 when the company faced a market value decline of more than 50%. This serves as a valuable lesson, one that I hope resonates through this chart. Even the most successful companies in the market have encountered periods of intense negativity from investors, presenting attractive opportunities for value-focused investors.
AI: From Promise to Profits
Considering that much of NVIDIA's recent surge is attributed to discussions surrounding AI, it is worth taking a detour to examine why AI has become such a powerful driving force in the market. Additionally, we can look to the past for guidance on how it will impact investors and businesses.
Revolutionary or Incremental Change?
As someone who has witnessed major disruptions in both my personal life and portfolio, such as the advent of personal computers in the 1980s, the dot-com/online revolution in the 1990s, followed by smartphones in the early 2000s and social media in the last decade, I have become both a believer and a skeptic of revolutionary changes in markets. These transformations not only impacted various businesses, both positively and negatively, but also altered the ways we live, work, and interact. Simultaneously, we have witnessed more incremental changes that, while significant in creating new businesses and disruptions, do not quite reach the level of revolutionary. Differentiating between the two is a challenging task, as failing to do so can lead to a loss of perspective and investment mistakes. Consequently, I was hesitant to view the "Cloud" and "Metaverse" businesses as revolutionary, perceiving them as more incremental changes.
So, where does AI fall on the spectrum from revolutionary to incremental or minimalist change? A year ago, I would have classified it as incremental. However, my perspective has shifted after observing the impact of ChatGPT. Although ChatGPT may not represent cutting-edge AI technology, it has made AI relatable to everyone. Witnessing my wife, a fifth-grade teacher, navigate students using ChatGPT for homework assignments, and experiencing my own students asking ChatGPT questions about valuation that they would have previously asked me directly, I recognize the potential for AI to disrupt our lives and work. However, it remains challenging to distinguish between the hype and the actual reality.
Business Implications
If AI represents revolutionary change and will be a key market driver in this decade, what does it mean for investors? Reflecting on the revolutionary changes of the past four decades (PCs, dot-com/internet, smartphones, and social media), there are lessons that may have relevance to the AI business.
A Net Positive for Markets? Does revolutionary change benefit the overall economy and/or equity markets? The results from the past four decades are mixed. The PC-driven tech revolution of the 1980s coincided with a decade of high stock market returns, as did the dot-com boom in the subsequent decade. However, the first decade of this century was one of the worst in market history, marked by stagnant stock prices. Stocks rebounded strongly over the past decade, with technology leading the way. When considering the entire period of change from 1980 to 2022, the annual return on stocks has been slightly higher than in the preceding five decades.
The impact of AI on the economy and equity markets remains uncertain, but history suggests that revolutionary changes can have a significant influence on market performance.
Given the volatility of the equity market, it's important to recognize that four decades is a relatively short time period to draw definitive conclusions. However, we can ascertain that technological changes have generally had a net positive impact on markets, albeit accompanied by increased volatility for investors.
A Few Big Winners and Many Wannabes and Losers: Undeniably, each revolutionary change in the past four decades has created winners within their respective industries. However, certain caveats have emerged. Firstly, these changes have given rise to industries dominated by a few major players, such as software, online commerce, smartphones, and social media. Secondly, the early leaders in these industries have often been overshadowed by new contenders. Lastly, while these industries have enjoyed overall success, they have also witnessed their fair share of false starts and failures. For investors, the key takeaway is that investing in revolutionary change ahead of others does not guarantee high returns if one backs the wrong players or misses out on the big winners. While NVIDIA and Microsoft have been acknowledged as significant players in the AI field, it remains entirely possible that different winners will emerge in the coming decade. Additionally, during the hype phase, many companies attempt to adopt an AI persona, similar to how companies sought a dot-com presence in the 1990s or claimed to have "user-intensive" platforms in recent years. As investors, differentiating the promising companies from the rest will become increasingly challenging in the future. Buying a portfolio of AI-benefiting companies with hopes of profiting from the few successful ones is more likely to result in an overpriced portfolio based on past market experiences.
Disruption: Technological change has left a trail of disrupted businesses in its wake. Investors in these disrupted companies not only suffer financial losses as a result but often compound their losses by further investing in these companies due to their perceived "cheapness." This pattern was observed in brick-and-mortar retail companies devastated by online retail and traditional newspaper/ad companies upended by online advertising. If AI fulfills its promise, there will undoubtedly be businesses disrupted by its advancements. We are currently in the hype phase, where promises often exceed actual delivery. However, the main targets of disruption will likely become clearer in the near future.
In conclusion, even if we agree that AI will revolutionize business and individual behavior in the coming years, there is no low-risk path for investors to capitalize on this belief.
Value Effects
Drawing from historical patterns, we are currently in the hype phase of AI, where it is oversold as a solution to almost every problem, leading to large price premiums for companies associated with AI. Paradoxically, these premiums are justified by the argument that there is too much uncertainty about how AI will impact future financials. This contradicts the notion of paying upfront for uncertain expectations. Hence, if one pays a high price for an AI effect in a company, it is crucial to overcome aversion to making estimates and utilize judgment and data to assess the impact of AI on cash flows, growth, risk, and ultimately, value.
In estimating these effects, it is sensible to categorize AI companies based on their position within the AI ecosystem. The following breakdown is suggested:
Hardware and Infrastructure: Like previous technological changes, AI necessitates hardware and infrastructure. For example, the AI effect on NVIDIA stems from increased demand for AI-optimized computer chips. As this market is projected to grow exponentially, companies that capture a significant share of this market will benefit. Additionally, other infrastructure investments will be necessary to actualize the AI promise, and companies involved in delivering such infrastructure are poised to gain.
Software: AI hardware alone holds little value without software that can effectively utilize its computing power. AI software can take various forms, including AI platforms, chatbots, deep learning algorithms (such as image and voice recognition and natural language processing), and machine learning. While this segment of the AI business may have more uncertainty, it also presents greater upside potential compared to hardware for the same reason.
Data: Since AI heavily relies on vast amounts of data, there will be businesses that derive value from collecting and processing data specifically for AI applications. The concept of big data, often used as a buzzword rather than a concrete business proposition in the past decade, may find its place in the value chain when combined with AI. However, the path to achieving this synergy will not be linear or predictable.
Applications: For companies that primarily consume AI rather than develop it, the promise of AI lies in its potential to transform business operations, presenting both positive and negative implications. AI proponents argue that it can reduce costs by replacing manual labor with AI-driven applications, leading to improved efficiency and increased profitability. However, even if we assume the first claim to be true (though AI replacements may not be as efficient or cost-saving as promised), I am skeptical of the second claim. If every company adopts AI and achieves cost reductions and efficiency improvements, it is more likely that prices for products/services will decrease, resulting in lower profitability for businesses. As one of my favorite sayings goes, "If everyone has it, no one does." Thus, if AI succeeds, it may, on aggregate, reduce overall profitability. Additionally, the automation potential of AI could be damaging or even devastating to existing companies that currently derive value from providing these services for lucrative fees. In such cases, AI will not only be a zero-sum game but a negative-sum one.
Regarding the impact of AI on investing, in general, and active investing, in particular, I believe that AI can enhance valuation and investing when used as a tool. I am eager to leverage AI in developing valuation narratives and numbers. However, for active investors, both individuals and institutions, AI will likely make the game even more challenging. Any edge an active investor possesses will be quickly replicated in an AI-dominated world. Given that AI tools will become accessible to all investors, AI alone will not provide a sustainable advantage for active investors.
Social Effects.
Will AI make our lives easier or more difficult? Will it make the world a better or worse place? Advocates of AI paint a picture of AI as a force for good, taking over mundane tasks and offering unbiased data analysis to facilitate better decision-making. On the other hand, some view AI as a tool for large companies to control minds and amass power. Based on our recent experiences with significant changes, I believe both perspectives hold truth. AI will have positive impacts in certain occupations and aspects of our lives while also creating unintended adverse consequences in other areas.
Some believe that AI can be regulated to serve its more noble purposes. However, I am less optimistic for several reasons. Firstly, regulators and legislators often lack a deep understanding of AI mechanics, making it challenging for them to implement sensible restrictions on its usage. Even if they do take action, their motives may not be purely altruistic. Secondly, any regulations or laws aimed at curbing AI's excesses will likely lead to unintended consequences, potentially exacerbating the problems they sought to address. Lastly, considering how regulators and legislators have struggled to handle the consequences of the social media explosion, I am skeptical of their ability to effectively regulate AI. While this perspective may appear pessimistic, I believe it is a realistic one. As consumers of AI products and services, we must strive to draw lines and differentiate between the positive and negative aspects of AI. Success may be uncertain, but we have no choice but to make an effort.
The AI Chip Story.
The AI narrative is particularly relevant to NVIDIA because, unlike many other companies, NVIDIA has already established substance and a target market within the AI space. In recent years, NVIDIA has invested heavily in developing products for the nascent AI market, which has positioned the company as a market leader. The surge in NVIDIA's data center revenues, driven significantly by AI chips, has sparked excitement in the market. While the company does not explicitly disclose the portion of data center revenues attributed to AI chips, estimates suggest that NVIDIA holds a dominant market share of around 80% in the $15 billion AI chip market of 2022. If these estimates are accurate, the majority of NVIDIA's data center revenues in 2022, totaling $15 billion, are derived from AI-optimized chips.
The impact of ChatGPT on market expectations has fueled growth projections for the AI chip market over the next decade. Estimates for the overall AI chip market in 2030 range from $200 billion to nearly $300 billion. Although considerable uncertainty surrounds these estimates, two assertions can be made regarding NVIDIA's presence in this business. First, the AI chip market will drive NVIDIA's revenue growth over the next decade, even as growth in gaming and other chip segments stabilizes. Second, NVIDIA currently enjoys a lead over its competitors, and while AMD, Intel, and TSMC will invest resources to bolster their AI businesses, NVIDIA's dominance is unlikely to waver easily.
NVIDIA: Valuation and Decision Time.
Considering NVIDIA's growth and success over the past decade, as well as its recent achievement of joining the exclusive club of trillion-dollar market cap companies, two competing impulses arise. On one hand, there is a desire to extrapolate past performance and assume that the company will not only continue to succeed but exceed market expectations. On the other hand, one might argue that the high expectations set by NVIDIA's past achievements make it difficult for the company.
Indeed, there can be different perspectives and varying opinions on the total market size and market share for the AI and Auto businesses. In my analysis, I will revisit these factors and their effects. I will continue to assume a 15% annual growth rate for gaming and other business revenues, which accounts for the contributions from additional ventures such as the omniverse, supporting overall top-line growth.
Profitability: In my NVIDIA story, I will focus on the R&D adjusted margin as the key profitability metric. Despite a setback in 2022, I expect NVIDIA to quickly bounce back and achieve higher margins than its peer group. While my target R&D adjusted margin of 40% may seem high, it is worth noting that the company achieved margins of 42.5% in 2020 and 38.4% in 2021. Given NVIDIA's reliance on TSMC for chip production, any margin increases will likely come from price increases rather than cost efficiencies.
Investment Efficiency: NVIDIA has made significant investments in the past decade, but its sales to invested capital ratio stood at 0.65 in 2022, indicating that it generated 65 cents in revenue for every dollar of capital invested. However, with the company's larger scale and the payoff from previous investments boosting revenues, I believe NVIDIA's sales to invested capital ratio will approach the global industry median, which is $1.15 in revenue for every dollar of capital invested.
Risk: The semiconductor industry is inherently cyclical, even for successful companies like NVIDIA. This cyclicality contributes to a higher cost of capital compared to the median company. Although I estimated NVIDIA's cost of capital to be 13.13% based on its geographic exposure and low debt ratio, I chose to use the industry average for US semiconductor companies, which is 12.21%, as the cost of capital during the initial growth period. Over time, I expect the cost of capital to gradually decline towards the overall market average of 8.85%.
Based on these assumptions and inputs, the estimated value for NVIDIA is as follows:
To further analyze the impact of varying assumptions on NVIDIA's value, I conducted a simulation considering the key inputs, including revenues. In the base case valuation, assuming strong growth in the AI and Auto Chip businesses with NVIDIA maintaining a dominant share in AI and a significant share in Auto, the projected revenues for 2033 amounted to $267 billion. However, it's important to note that these estimates rely on assumptions about future market conditions, which are subject to uncertainty.
In order to account for this uncertainty, I incorporated distributions for each segment of NVIDIA's revenues in the simulation. By running multiple iterations of the simulation, I can observe how different scenarios affect the estimated value per share. This allows for a better understanding of the potential range of outcomes and helps to assess the breakeven points for the company.
If there are specific aspects or further details you would like me to delve into, please let me know.
Taking into account the uncertainties and varying assumptions, the simulation reveals a wide range of potential outcomes for NVIDIA's value. In some scenarios, the projected revenues for 2033 exceed $600 billion, while in others, they fall below $100 billion.
Regarding operating margin, the base case assumes an increase to 35% next year and a target operating margin of 40% in 2027, which is maintained indefinitely. However, there is uncertainty surrounding these assumptions, with potential for margins to be higher or lower. The distribution used for the target operating margin reflects this uncertainty, ranging from 30% to 50%.
The reinvestment aspect is determined by the sales to capital ratio. While the base case assumes a ratio of 1.15, which is the semiconductor industry average, it is possible for NVIDIA to either continue reinvesting at a rate closer to its historical average (0.65) or reduce reinvestment due to prior investments made in the AI chip business. The simulation considers these possibilities, with a range of sales to capital ratios from 0.65 to 1.94.
Regarding risk, the focus is on the cost of capital, which is centered around the industry average of 12.21% used in the base case. However, there is potential for the growing AI business to reduce revenue cyclicality, leading to a lower cost of capital closer to the market average of 8.85%. Conversely, increased uncertainty in the AI market could raise the cost of capital towards 15%, reflecting higher risk.
Given these inputs and the simulation, the resulting range of values per share for NVIDIA is displayed in the analysis.
To evaluate the likelihood of NVIDIA's intrinsic value exceeding $400 per share, it is necessary to examine the two key variables that drive its value: revenues in the 10th year and operating margins.
The analysis reveals that in order for NVIDIA's intrinsic value to surpass $400, it would require a combination of significantly higher revenues and operating margins compared to the base case assumptions. These higher values would need to align with more optimistic scenarios, such as the upper percentiles of the revenue and margin distributions.
While it is not impossible for NVIDIA to reach such levels, the probability, based on the provided estimates, is relatively low. The current stock price is approaching the 95th percentile of the value distribution, indicating that it is already reflecting a more optimistic outlook for the company's future performance.
The table presented supports the conclusions drawn from the simulation, highlighting that while there are possible scenarios where the current stock price of NVIDIA could be considered fair or undervalued, these scenarios require an extraordinary combination of revenue growth and exceptionally high profit margins. A target margin of 50% would be considered challenging to achieve in the semiconductor industry. If NVIDIA is to justify its current pricing, it would likely require explosive revenue growth. In essence, the company would need to identify additional markets with potential similar to that of the AI market, where NVIDIA can establish a dominant market share.
NVIDIA as an Investment
I have a positive view of NVIDIA as a company and commend Jensen Huang's leadership. The company has demonstrated the ability to achieve growth and maintain high-profit margins in an industry where growth was becoming scarce and certain market segments were becoming commoditized. NVIDIA's success can be attributed to strategic foresight rather than mere luck, positioning the company to capitalize on opportunities in the gaming and crypto industries. Looking ahead, the growth potential in AI and Auto businesses further supports NVIDIA's optimistic outlook. However, even with this positive narrative, my valuation analysis suggests that the current stock price exceeds its intrinsic value.
As an investor with a value-oriented philosophy, it is not easy to sell a highly successful investment like NVIDIA. Although there is a low-probability scenario where the company could continue to deliver strong returns at current prices, I have chosen to sell half of my holdings and secure profits, while holding onto the remaining half for potential future market opportunities. Some may argue that this approach is inconsistent with a pure value philosophy, but I consider myself a pragmatist and find this strategy suitable for my circumstances. This decision raises an interesting question about holding a stock in one's portfolio that wouldn't be purchased at current prices, and I intend to explore this topic further in a future post.
NVIDIA as a Trade
I have emphasized the philosophical distinction between investing and trading in my previous posts. Investing involves assessing a stock's value, comparing it to its price, and acting on the discrepancy, aiming to profit as the gap between value and price closes. On the other hand, trading focuses on buying at a lower price and selling at a higher price, regardless of the reasons behind price movements or their rationality.
Taking the difference between a stock's value and its price into consideration, it is understandable why some investors may choose to trade NVIDIA rather than solely focus on its intrinsic value. Even if one believes that NVIDIA is currently overvalued, they may still opt to buy the stock based on expectations of continued price appreciation driven by momentum or incremental information. The momentum effect can generate significant returns over the short to medium term, and it is reasonable to enjoy the gains without being hindered by those who emphasize value considerations. However, it is essential to acknowledge that momentum is not a perpetual force, and it can lead to a reversal in stock price. Traders should remain vigilant for indicators that signal a shift in momentum, rather than fixating solely on value.
The lesson is prepared for you by Site: Technical Buddy
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