Why the Nasdaq may not capture the full growth potential of AIThe start of 2023 has marked the return of tech growth stocks alongside the surge of generative artificial intelligence (AI) and large language models (LLMs). The biggest tech companies in the world have benefitted from the buzz created by ChatGPT and rapidly rising enthusiasm around AI in general. Nvidia, a leading semiconductor company seen as one of the main AI beneficiaries, has advanced the most within the Nasdaq-100 and even joined the trillion-dollar market cap club just weeks ago.
The year-to-date rally of ‘Big Tech’, led by Nvidia, has resulted in a strong return differential of 22.33%1 between the widely followed tech gauge (the Nasdaq-100) and the broad equity exposure (the S&P 500). The top 10 holdings in the Nasdaq-100 by contribution to return (CTR) have jointly posted 30.45% year-to-date, representing more than 82% of the total index return. This advance of the top Nasdaq-100 holdings, capitalising on the buzz around AI, is begging the question from investors whether allocation to the Nasdaq-100 already offers good exposure to the long-term investment potential associated with the AI megatrend.
To answer this question, we have to take a step back and think of the concept of megatrends and benchmarks in the portfolio construction process. Benchmarks are usually viewed by investors as a core allocation, while thematic investing is being used as a return enhancement play that benefits from the evolution of various megatrends. In the case of the Nasdaq-100, we can point to several arguments why a thematic strategy focused on the AI theme might be a better option if an investor’s goal is to benefit from the long-term growth potential offered by AI.
1. The AI space represents a wide variety of areas that can achieve wider adoption and success at various points in the future. A targeted AI strategy can build exposure to the theme and its evolving trends through a diversified basket of more pure-play companies involved in various AI activities. In turn, the Nasdaq-100 will tap into the space only through a handful of companies that would offer a less comprehensive and less pure exposure to the theme.
2. A targeted AI strategy has the potential to capture the mega caps of tomorrow early on and with a meaningful weight within the portfolio. Investing in AI through the Nasdaq-100 might be seen by investors as a safe way to avoid losers and focus on more successful AI companies that made it into the benchmark. However, this approach does not allow investors to reap the return potential associated with exciting smaller companies early on. After all, the growth potential driving the returns in the tech space is highest for smaller and younger companies.
Investing Tesla and Nvidia (the latest two companies that managed to hit a $1 trillion market cap) in them 3 months after they went public would have resulted in much higher annualised returns in comparison to returns after they joined the Nasdaq-100. In addition, it took both companies around 2-3 years to join the tech benchmark and, after they did, their starting weights were only 0.40%-0.50%. In contrast, thematic strategies might invest in companies shortly after their IPO (initial public offering) dates and might allocate a more meaningful weight to them.
3. A satellite thematic exposure can improve the risk-adjusted portfolio returns through increased diversification. The concept of diversification was first formalised by H. Markowitz as early as in 1952. However, in practice, it’s not feasible to hold all stocks in the investable universe and investors stick to broad benchmarks to build their market exposure. In this situation, thematic investing represents a novel way to split the universe of investable companies and identify promising opportunities aligned with megatrends shaping our future. Relatively low overlap of thematic strategies with broad benchmarks is what makes them particularly attractive for a satellite exposure.
Trying to kill two birds with one stone (that is, building a core tech exposure and capturing the potential of the AI theme) by using the Nasdaq-100 could backfire. It could deteriorate diversification and risk-adjusted returns for two reasons: 1) Sticking just to AI companies within the Nasdaq-100 narrows down the return drivers associated with the AI megatrend; 2) Investors increase idiosyncratic risks in their portfolios associated with the biggest tech companies, most likely captured in some other portfolio allocations, for example, the S&P 500.
Thematic strategies specifically focused on AI might represent a better option for investors seeking to benefit from the long-term growth potential associated with the megatrend in contrast to the theme exposure offered through the Nasdaq-100. When selecting the specific AI strategy it’s important to understand how each strategy captures the space and to align it with investor’s beliefs about the future development of the megatrend. Diversification benefits and potential return drivers associated with the theme are yet other important considerations that help to govern the strategy selection process.
Sources
1 As of 27 June 2023.
This material is prepared by WisdomTree and its affiliates and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date of production and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by WisdomTree, nor any affiliate, nor any of their officers, employees or agents. Reliance upon information in this material is at the sole discretion of the reader. Past performance is not a reliable indicator of future performance.
AI
TESLA LONG AT THE PARABOLIC INFLECTION POINTmy thesis is that Tesla is now a matured, deep moated, multi-sector innovation enterprise
areas of focus
transporation
manufacturing
commodities
logistics
big data
synthesizations
memetics
artifical intelligence
debt leverage
decentralization
neo-feudal globalization
I'm Long Here.
C3.ai (AI) Looking Ready 4 Another ClimbI recon C3.ai have found another ladder to start climbing again after an inevitable lul in the market due to the initial 'hype' related to AI and c3.ai in particular had subsided only to leave the well known AI driven tech company to show why they are still a good investment through actual company performance as well as the fact that this week there was some good news from Oracle who gave AI stocks a boost, as the enterprise tech giant said strong cloud sales were being boosted by generative AI. That helped propel the broad market to a banner week, and C3.ai shares were up 24.4% for the week. So with all that inside info as well as the indicators on this chart you see here, Houston seems to be echoing in my analytical mind.
$LYFT getting ready to $LFFT offBullish divergence on the annual time frame, that spans across a 2 year period.
Stochastic indicating the market is over sold on the annual time frame and that bulls are re entering the market.
RSI indicating market exhaustion to the downside and bulls returning to the market,
LYFT market dominance in the self driving/ ride sharing app is growing. Also with the expansion of self driving, company expense will drop and profits will rise.
AGIX – Looking Great! Possible 90% ProfitTLDR:
• A successful break out a of a Falling Wedge Formation could lead to an 90% profit long trade.
Background:
• AGIX has been in a correction since February 8th, 2023. This correction is creating a Falling Wedge formation.
• Since my previous AGIX idea, AGIX corrected further and now it reached the 0.382 Fibonacci retracement.
• The target of a Falling Wedge is the measure from low to high of the wedge’s beginning.
• Considering that the AI narrative is the darling of investors at the stock market and crypto, I think AGIX could go much higher. However, let’s not count the chickens before they are hatched. A cool 90% profit is enough, for now.
My Trade Idea:
• Entry: 0.26 (approximate number, breakout of the wedge).
• SL: Daily candle below the resistance line.
• TP: 0.34, 0.46, 0.51. Leave a moon bag.
God willing, I will update you as AGIX makes further progress.
NFA.
What do you think? Please share in the comments.
Best wishes to all.
Investing In Artificial Intelligence (AI) : Beginner’s GuideThe field of artificial intelligence (AI) has emerged as a highly attractive investment option, captivating the attention of investors worldwide. With its capacity to reshape industries and drive innovation, AI has gained prominence as a transformative technology. By simulating human intelligence and performing intricate tasks, AI is revolutionizing sectors ranging from transportation to finance and beyond. Given the rapid growth of the AI market, which is projected to reach revenues of up to $900 billion by 2026, having a comprehensive understanding of how to invest in this dynamic field has become crucial for investors.
In this comprehensive guide tailored for beginners, we will delve into the fundamentals of AI, exploring its underlying concepts, methodologies, and applications across various industries. By gaining insight into the inner workings of AI, investors can grasp the potential impact it can have on different sectors, enabling them to identify promising investment opportunities.
Moreover, we will examine diverse investment strategies that investors can consider when venturing into the AI market. These strategies will encompass a range of approaches, from investing in established AI companies and technology giants, to exploring opportunities in startups and early-stage ventures that are driving innovation in the AI space. Additionally, we will explore investment vehicles such as AI-focused exchange-traded funds (ETFs) and mutual funds, providing investors with a broader exposure to the AI market.
Throughout this guide, we will address the key factors to consider when investing in AI, including the evaluation of AI technologies, understanding regulatory and ethical implications, and staying updated with the latest industry trends. By equipping investors with the necessary knowledge and insights, this guide aims to empower them to make informed investment decisions in the dynamic and evolving landscape of AI.
As AI continues to redefine industries and shape the future, investing in this transformative technology presents an exciting opportunity for investors seeking long-term growth and exposure to cutting-edge innovation. Through this beginner's guide, we invite investors to embark on a journey into the world of AI investment, unlocking the potential for both financial returns and contributions to the advancement of society as a whole.
Artificial Intelligence (AI) Explained
Artificial Intelligence (AI) has emerged as a groundbreaking technology that aims to replicate human intelligence in computers and machines, surpassing human capabilities in terms of speed and accuracy. Leading companies like Microsoft (MSFT) and Google (GOOGL) utilize AI to develop systems capable of problem-solving, answering inquiries, and executing tasks that were traditionally performed by humans.
The advancement of AI systems has expanded their applications across diverse industries and sectors. One notable transformation is occurring in the transportation industry, where electric and autonomous vehicles are revolutionizing travel and poised to contribute trillions of dollars to the global economy. In the banking sector, AI is employed to enhance decision-making processes in high-speed trading, automate back-office functions such as risk management, and even introduce humanoid robots in branches to reduce costs. These examples only scratch the surface of the extensive range of AI applications.
Analysts at International Data Corp. (IDC), a renowned market intelligence provider, project that the AI market will generate global revenues of up to $900 billion by 2026. This estimate reflects a significant compound annual growth rate of 18.6 percent from 2022 to 2026, underscoring the exponential growth potential of AI.
What was once considered a luxury has now become an essential component of modern businesses. The global pandemic has accelerated the adoption of AI, making it pervasive across all aspects of business operations. From healthcare and manufacturing to finance and customer service, AI has demonstrated its value in enhancing efficiency, optimizing processes, and driving innovation.
Investing in AI presents an opportunity to capitalize on its transformative potential. However, it is essential for investors to approach AI investments with a thorough understanding of the technology, its applications, and the companies leading the way. As AI continues to shape industries and redefine the future, investors who navigate this dynamic landscape stand to benefit from its long-term growth and the potential for significant returns.
How To Invest In Artificial Intelligence
As a retail investor, you may already have exposure to artificial intelligence (AI) through various prominent U.S. public companies that utilize AI or invest in this technology. However, if you're specifically interested in investing in AI, there are several approaches you can consider:
Individual Stocks: Conduct thorough research and invest directly in companies that specialize in AI development, application, or integration. Look for companies with a strong focus on AI, a robust research and development program, and a history of innovation in the field.
Exchange-Traded Funds (ETFs): Explore AI-focused ETFs that concentrate on companies involved in AI technologies. These funds offer diversification by investing in a portfolio of AI-related stocks, providing exposure to a broad range of companies in the AI sector.
Index Funds: Invest in broad market index funds that include leading companies at the forefront of AI development. These funds track major market indices like the S&P 500, which often include prominent players in the AI industry.
Additionally, Contract for Difference (CFD) trading is another option for investing in AI. CFDs allow you to speculate on the price movements of AI-related assets without actually owning the underlying assets. By taking long or short positions, you can potentially profit from both upward and downward price movements in the AI sector. However, it's important to note that CFD trading carries a higher level of risk and requires a good understanding of market dynamics.
Top AI Stocks To Consider:
Microsoft (MSFT)
As of May 2023, Microsoft, the renowned developer of the Windows operating system, holds the position of the largest Artificial Intelligence (AI) company. In recent times, Microsoft has made significant strides in the field of AI, unveiling a range of new features and initiatives across its product line.
One notable development is the integration of AI-powered enhancements into Edge, Microsoft's web browser. These enhancements leverage AI technology to provide users with improved browsing experiences, including enhanced performance, personalized recommendations, and advanced security features.
Furthermore, Microsoft has incorporated AI capabilities into Bing, its search engine. The integration of AI allows Bing to deliver more accurate and relevant search results, enhancing the overall search experience for users.
Highlighting its commitment to AI, Microsoft announced a substantial investment in OpenAI, the creator of ChatGPT, a widely used language model. This multiyear and multibillion-dollar partnership have resulted in the deployment of OpenAI models across Microsoft's product range, including the Azure OpenAI Service. Additionally, Microsoft's Azure cloud platform serves as the exclusive provider for OpenAI's cloud-based services.
By investing in OpenAI and integrating AI capabilities into its products and services, Microsoft aims to harness the power of AI to deliver innovative solutions and enhance user experiences. This strategic focus on AI demonstrates Microsoft's recognition of the transformative potential of this technology and its dedication to remaining at the forefront of the AI industry.
Tesla (TSLA)
In the realm of electric vehicles (EVs), Tesla stands apart from technology giants like Microsoft and Alphabet by leveraging AI and robotics to drive innovation. The company has positioned itself as a leader in self-driving cars, an area heavily reliant on AI for tasks such as visual processing and strategic planning.
Tesla is actively pursuing the development of self-driving technology and has been working on AI inference chips that are specifically designed to run its full self-driving software (FSD). These chips enable efficient and powerful processing capabilities, enabling Tesla vehicles to make real-time decisions and navigate autonomously.
Beyond self-driving vehicles, Tesla has expanded its AI endeavors into the realm of humanoid robots. In October 2022, CEO Elon Musk unveiled "Optimus," a highly anticipated robot. Musk envisions a future where Tesla's robot business surpasses the value of its cars, indicating a broader ambition to extend beyond the automotive industry.
In addition to self-driving technology and robotics, Tesla is actively involved in various AI fields. This includes the development of Dojo chips and systems, which aim to enhance AI training and accelerate computational processes. Tesla also focuses on neural networks, autonomy algorithms, code foundations, and evaluation infrastructure to continuously improve and refine its AI capabilities.
By applying AI and robotics to the EV industry, Tesla is at the forefront of technological advancements and aims to shape the future of transportation. Its commitment to developing cutting-edge AI solutions demonstrates the company's dedication to pushing the boundaries of innovation and redefining the possibilities within the automotive industry.
IBM (IBM)
In May 2023, IBM, a computing giant with a long-standing history in the technology industry, made a significant announcement regarding its platform called Watsonx. This platform is designed to empower developers by providing them with a comprehensive set of tools for creating AI models.
Watsonx equips developers with machine learning tools, foundational models, hardware resources, and data storage capabilities, enabling them to build sophisticated AI applications. By offering a range of resources within a unified platform, IBM aims to streamline the AI development process and make it more accessible to developers.
In collaboration with Hugging Face, a prominent provider of open-source AI libraries, IBM has integrated the benefits of Hugging Face's libraries and extensive collection of open models and datasets into the Watsonx.ai studio. This collaboration allows developers to leverage Hugging Face's resources and tap into a vast array of pre-trained models and datasets, accelerating the development of AI solutions.
Beyond its AI offerings, IBM has been actively involved in AI integration research. The company's Global AI Adoption Index explores the impact of AI adoption on businesses and society as a whole. This research initiative aims to provide insights into the current state of AI adoption, identify trends, and understand the potential implications of AI on various industries and sectors.
IBM's commitment to advancing AI technology, as demonstrated by its Watsonx platform and research initiatives, highlights the company's ongoing efforts to drive innovation and facilitate the integration of AI into diverse domains. By empowering developers and exploring the broader implications of AI adoption, IBM continues to play a significant role in shaping the future of artificial intelligence.
Alphabet (GOOGL)
Alphabet, the parent company of Google, has been actively investing in the AI sector, demonstrating its commitment to advancing artificial intelligence technologies. In April, Alphabet's venture capital subsidiary, CapitalG, played a leading role in a $100 million funding round for AlphaSense, an AI startup. This investment not only highlights Alphabet's financial support for AI innovation but also strengthens its presence in the AI industry.
In addition to its investment activities, Google, as a part of Alphabet, has made substantial investments in other AI-related companies. For instance, Google has invested approximately $400 million in Anthropic, a competitor to ChatGPT, further expanding its involvement in the AI landscape. Furthermore, Google has acquired Alter, a startup specializing in AI avatars, which showcases its strategic focus on enhancing AI capabilities and exploring new applications for the technology.
Within its own product ecosystem, Google has introduced various generative AI tools that leverage the power of artificial intelligence. One notable example is Bard, Google's own counterpart to ChatGPT, which provides real-time access to information from the web. This demonstrates Google's efforts to develop AI models capable of generating dynamic and contextually relevant content.
Moreover, Google is incorporating AI functionality into its Workspace suite, starting with popular tools like Gmail and Google Docs. By integrating AI capabilities into these productivity tools, Google aims to enhance user experiences, improve efficiency, and enable new possibilities for collaboration and content generation.
Alphabet's investments in AI startups, acquisitions, and the development of generative AI tools highlight the company's dedication to harnessing the potential of artificial intelligence. Through these initiatives, Alphabet continues to shape the AI landscape and drive innovation in the field.
Amazon (AMZN)
Amazon, a prominent player in the AI field, has established itself as a leader by offering a comprehensive suite of AI and machine learning (ML) services through its cloud computing platform, Amazon Web Services (AWS). AWS provides a wide range of tools and services that empower developers and businesses to integrate AI and ML functionalities into their applications and workflows efficiently.
Notably, Amazon not only provides AI services to other businesses but also harnesses AI capabilities within its own operations. For instance, the company employs sophisticated AI algorithms in its online store to deliver personalized product recommendations to customers, creating a more tailored and engaging shopping experience.
One of Amazon's most recognizable AI applications is Alexa, the virtual assistant powering Echo devices. Powered by natural language processing and ML algorithms, Alexa can comprehend and respond to user commands, enabling users to interact with their devices using voice commands. This integration of AI technology has revolutionized the way people interact with their smart devices and has become a prominent feature in many households.
Amazon's commitment to AI is further evident through its ongoing investments in AI research and development. The company continually seeks to advance AI technologies, exploring new applications and improving existing capabilities. By embracing AI in various aspects of its business, Amazon aims to enhance customer experiences, drive innovation, and remain at the forefront of AI integration in the industry.
Oracle (ORCL)
Oracle (ORCL), a renowned provider of cloud computing solutions, has emerged as a leading player in the AI landscape by offering the Oracle Cloud Infrastructure. This robust cloud platform serves as the foundation for various workloads, including AI applications, empowering businesses to leverage the benefits of AI technology.
Recognizing the growing significance of AI, Oracle has taken steps to enhance its AI capabilities for enterprise customers. Notably, the company has expanded its collaboration with Nvidia, a prominent chipmaker specializing in AI hardware. This strategic partnership allows Oracle to harness the power of Nvidia's advanced AI-focused GPUs (Graphics Processing Units) and other hardware technologies.
By integrating Nvidia's hardware into its infrastructure, Oracle aims to deliver enhanced AI performance to its enterprise customers. This collaboration equips businesses with the ability to process vast datasets and execute complex AI algorithms more efficiently, leading to improved insights and outcomes. By leveraging Nvidia's powerful AI hardware, Oracle demonstrates its commitment to providing cutting-edge AI solutions that address the evolving needs of businesses in the era of digital transformation.
Through its collaboration with Nvidia and its focus on advancing AI capabilities, Oracle solidifies its position as a leading provider of AI-enabled cloud infrastructure and reinforces its commitment to empowering businesses with the tools and technologies needed to harness the potential of AI in their operations.
How To Select The AI Stocks To Invest In :
When selecting AI stocks to invest in, it's important to conduct thorough research and consider various factors. Here are some key considerations to help guide your decision-making process:
1) Company's fundamentals: Review the financial health and performance of the company. Analyze its financial statements, including the balance sheet, income statement, and cash flow statement. Look at key indicators such as the price-to-earnings (P/E) ratio, return on equity (ROE), and debt-to-equity (D/E) ratio to assess its profitability and financial stability.
2) Technical analysis: If you're a short-term trader, utilize technical analysis to study price patterns and trends. Use technical indicators and candlestick charts to identify entry and exit points based on historical price movements.
3) Analyst ratings: Consider the latest analyst ratings and commentary on specific stocks. Analyst opinions can provide valuable insights, but keep in mind that they are subjective and should be considered alongside other factors.
4) Latest company news: Stay updated on a company's news and developments. Look for announcements related to AI investments, acquisitions, R&D initiatives, and new product offerings. This information can indicate a company's growth potential and competitive positioning.
5) Competitive landscape: Assess the company's position within the AI industry and its competitive advantage. Consider its technology, market share, and ability to innovate. Evaluate how it compares to other players in the market.
6) Management team: Evaluate the leadership and management team of the company. Look for experienced executives who have a track record of success and a clear vision for the company's future.
7) Industry trends: Stay informed about the latest trends and advancements in the AI industry. Understand how AI is being adopted across different sectors and the potential impact it may have on the company you're considering.
8) Regulatory environment: Consider the regulatory landscape surrounding AI. Assess how regulations and policies may affect the company's operations and growth prospects.
9) Diversification: Manage risk by diversifying your investments across different AI stocks and sectors. This helps mitigate the impact of individual stock performance and provides exposure to a range of opportunities.
Conclusion:
Investing in AI presents unique opportunities for investors as this cutting-edge technology continues to transform industries and drive innovation. The potential for AI to revolutionize various sectors, enhance efficiency, and create new business models is immense. Whether through individual stock investments, AI-focused ETFs, index funds, or even CFD trading, investors can participate in the AI market and potentially benefit from its growth.
However, investing in AI requires careful consideration and research. It is important to understand the fundamentals of AI, including its applications and potential impact on industries. Analyzing company financials, such as balance sheets and income statements, can provide insights into the financial health and long-term potential of AI-focused companies.
Staying updated on industry trends, news, and developments is crucial. Monitoring AI-related investments, partnerships, research, and product advancements can help identify companies that are at the forefront of AI innovation.
Diversification is also key in AI investing. Spreading investments across different AI stocks, sectors, and geographies can help mitigate risk and capture opportunities in various segments of the AI market.
Lastly, it is important to remain informed and adaptable as AI technology continues to evolve. Regularly assessing and adjusting investment strategies based on market conditions and emerging trends is essential to capitalize on the transformative potential of AI.
By understanding the fundamentals, conducting thorough research, and staying informed, investors can position themselves to potentially benefit from the growth and impact of AI in the years to come.
What does real estate have to do with AI?To shed some light on the potential of artificial intelligence (AI), and discuss the role of the supporting infrastructure enabling this boom, we were delighted to leverage the expertise of Eric Rothman, Portfolio Manager, Real Estate Securities with CenterSquare. CenterSquare is a dedicated real estate investment manager, with around $14 billion under management, and Eric has been with the company for 17 years.
Before we explore the Nvidia story and the relationship between AI, data centres, and ‘new economy real estate’, let’s define what that latter phrase means.
New economy real estate is supporting technological advancements, like AI
What is ‘new economy real estate’? Eric noted that there is so much beyond the traditional ‘4 foodgroups’ of real estate:
1) retail
2) office
3) residential
4) industrial
When CenterSquare defines the ‘new economy real estate’ space, Eric noted that the larger components include data centres, cell phone towers, and warehouses dedicated to new economy logistics—things like ecommerce fulfillment. This is far from traditional, industrial real estate.
Some of the smaller segments include life sciences, cold storage, and office space that is uniquely tailored to technology tenants, typically located in specific cities with focused pools of technology talent. Such cities might be Seattle, San Francisco or New York. These types of ‘real estate’, most notably data centres, are vital to support growing technologies like AI.
The Nvidia story—$1 trillion to be spent?
There has been a huge amount of excitement and discussion around Nvidia as the stock has enjoyed overnight success on the coattails of the AI boom. ‘$1 trillion’ is a big number (and a nice headline), but it’s very difficult to forecast where generative AI will take us. Some people say it is like inventing the wheel or the personal computer. This is a big claim, and only time will tell.
If people are thinking about ‘data centre REITs’ as an investment, they have to understand that data centres just fulfil the provision of power, cooling, and connectivity. The data centre REITs do not actually own the computers. The tenants invest in the computers. One thing that is absolutely true, however, is that as an owner, you love to see the tenants putting money into the space that they are renting. Why? This makes it less likely they are going to leave. Therefore, a greater investment in AI technology and computing power may be a positive signal for the supporting real estate (like data centres).
Eric’s conclusion, whether thinking about the impact of generative AI on data centre REITs or cell phone tower REITs, was that the move in share prices hasn’t reflected where we could be going yet. Connectivity and data centres will be vital components for artificial intelligence, but it’s not yet clear how or when investors are going to reflect that in the real estate prices. Eric noted that investors frequently forget about the buildings until later in a cycle or a trend.
Greater computing power = greater energy consumption?
Another aspect that we discussed was energy usage. Eric estimated that newer AI-focused semiconductors draw more power, not just a little bit more power but a step change in power consumption.
A chart from the ‘Decadal Plan for Semiconductors’, a research report by Semiconductor Research Corporation allows us to compare compute energy to the world's energy production. A critical point to keep in mind is that ‘something has to give’; simply continuing to add computational capacity without thinking of efficiency or energy resources will eventually hit a wall. However, if history is any guide, we should expect that, as demand and investment in computational resources increases, there will be the potential for gains in efficiency, improved model design, and even different energy resources that may not yet exist today.
Since many investors may be less familiar with cell phone towers, Eric made sure to mention just how strong of a business model he believes this to be. Now, it’s true that these REITs have not performed well in the past 18-months, but we are right in the middle of the current 5G rollout. Tenants have long leases, there is lots of demand, and there are even consumer price index (CPI) escalators that increase the rent to be collected.
Conclusion: a different way to think about real estate
It was great to be able to spend some time speaking with Eric and to learn about what’s happening both in the broader real estate market as well as in the more specific, new economy, ‘tech-focused’ market. The full discussion is accessible on behind the markets podcast
This material is prepared by WisdomTree and its affiliates and is not intended to be relied upon as a forecast, research or investment advice, and is not a recommendation, offer or solicitation to buy or sell any securities or to adopt any investment strategy. The opinions expressed are as of the date of production and may change as subsequent conditions vary. The information and opinions contained in this material are derived from proprietary and non-proprietary sources. As such, no warranty of accuracy or reliability is given and no responsibility arising in any other way for errors and omissions (including responsibility to any person by reason of negligence) is accepted by WisdomTree, nor any affiliate, nor any of their officers, employees or agents. Reliance upon information in this material is at the sole discretion of the reader. Past performance is not a reliable indicator of future performance.
FET – Descending Wedge Breakout• FET broke out of the upper trendline of the Wedge.
• Final confirmation is above the previous wick at 0.2544 USD.
• The Wedge’s target is 0.3866.
• I expect a lot more from FET.
• Please read my previous FET Idea for more context.
NFA.
What do you think? Please share in the comments.
Best wishes to all.
Breakout Trade in SOUNI’ve had my eye on this stock for months.
SoundHound is smaller, off-the-radar AI stock. The company owns several patents involving voice-recognition technology.
If you drive a Dodge, Chrysler, Jeep, Hyundai, Kia, Honda or Mercedes-Benz, you are probably using their software already.
HOUN ripped higher to start the year, but it got overcooked and settled back in the $3 range.
It has been forming a base for the last four months with resistance near $3.35, and the stock is now trying to breakout higher.
Notice how the volume bars mimic the action from the price candles. Volume climbs as the stock moves higher and declines as price comes down.
This is exactly what we want to see. It shows the aggressive action is on the buy side and bulls are in charge.
I want to see this stock stay above $3.25 and remain above the base. Traders may consider buying on a pullback into the $3.50-$4.00 area.
QQQ Outlook 0626-30/2023Technical Analysis: Last week’s price action put NASDAQ:QQQ back inside the bullish channel we’ve been watching since March. We should see come corrective price action this week before tech runs higher.
Bulls will look to see if we can stay above last week’s lows at 360. It is crucial bulls hold this level or we could see the daily fair value gap that could be filled below at 357.66.
Bears will want to see a breakdown under the daily fair value gap, where we could test the strong monthly level at 354.43. If we lose the levels above, we can look for a test of the lower trendline in the upcoming weeks, and possibly a large gap to fill to the downside from 336.67-332.91. Inside this gap is the 50SMA and the 61.8% retrace at 334.00.
Upside Targets: 364.57 → 370.10 → 373.83 → 380.76 → 386.28
Downside Targets: 360.00 → 358.97 → 357.66 → 354.43 → 352.46
NVDA: Bearish Divergence at PCZ of Bearish Shark: Selling CallsWe have some Bearish Divergence on NVDA after reaching the PCZ of a 4 Hour Bearish Shark; if we get some serious followthrough I could see it going down to $400 or even all the way down to about $350
I will be selling multi-week calls around the strike of $425 and $435
Crypto's Impending Boom: Market Shifts and Global DynamicsCryptocurrencies in the Face of Rising Bond Yields and a Strengthening Dollar
Cryptocurrencies have been on a short-term downward trend, attributed to deteriorating liquidity within crypto and outside crypto due to rising bond yields and the strengthening dollar, as they are sensitive to rates and liquidity fluctuations. Their recent downturn can also be explained by the fact that they had performed much better than their interest rate & liquidity models had suggested and by US tech stocks sucking flows and liquidity.
Capital Flows: The Rising Crypto Tide in Hong Kong
Significant rallies in the crypto sector could be on the horizon, especially when the double bottoms in Bitcoin and Ethereum are swept. Some important reasons are the impending acceptance of crypto exchanges by Hong Kong and the return of cash to Voyager's creditors. As Chinese citizens grapple with capital outflows, liquidity flows from China could be redirected to the crypto sector through Hong Kong. At the same time, with mounting US-China tensions, cryptocurrencies could provide an alternative, potentially the only proxy investment to AI (US big tech).
In the Face of Uncommon Volatility: A Premonition of Crypto Spikes
As we navigate the debt ceiling crisis, we might experience volatility spikes, even though volatility remains subdued. Next week we might start seeing some significant moves, as USD 3.6 billion worth of options expired this Friday, constituting roughly 26% of Deribit's open interest. Implied volatility is at its lowest, with DVOL trading at 44 for BTC and ETH and shorter-dated even lower. This is relatively uncommon, and whenever we've seen such low volatility, a significant spike in vol has followed soon after.
A Confluence of Events: Setting the Stage for Crypto Price Surge
The latest spike in January coincided with a price rally, which may reoccur, given the significant expiration of mainly call options, with a Put/Call ratio of 0.38. With events such as Voyager distributing >1B in cash to creditors, Hong Kong authorizing crypto trade for its citizens, US tech investors capitalizing/diversifying on >3T gains and redirecting some into crypto, and potential issues with the US banking system or USD stablecoins due to a possible US default, the stage is set for a potentially explosive growth in crypto prices. The last part is something many ignore, but FUD, or real issues around banks or stablecoins, could recreate the conditions for another SVB - USDC type rally, as investors view Bitcoin and Ethereum as the safe havens of crypto and of the financial system broadly.
Bullish on Synergy: The Powerful Integration of AI and Crypto
The convergence of AI and crypto can create new business models, enhance decision-making processes, improve trust and transparency, and unlock organizational and operational efficiencies. Some areas where AI and crypto can synergize: AI-Powered Smart Contracts, New forms of financial tools, AI-to-AI financial transactions, Enhanced Security and Privacy both for AI and Cryptocurrencies and so on. AI will integrate and interact with open and trustless systems like crypto, but it's unlikely to interact with closed systems like banks. The confluence between the two technologies is apparent, making me bullish long-term.
Trade ideas
As mentioned in my recent ETHBTC idea, Ethereum looks stronger than Bitcoin. However, Bitcoin looks cleaner than Ethereum. Bitcoin has two critical untested areas lower: 25000-25700 - with 25200 and the double bottom at 25800 being the basic levels, and 22600-23600 - which is an area that the market didn't test appropriately as it went higher, especially 22600, which was the critical breakout level.
BTCUSD has two triple tops higher, one around 27600 and the other around 29900. It's unclear whether the double bottom will be swept first or one or both of the triple tops will be swept first, but to me, it's clear that the market will probably rally much higher once the bottom is swept. Given everything I mentioned above, it's better to bet on the upside and not short the market here. Therefore long around 25700 and cut below 24900, long around 23600 and cut below 22500, with targets at 27600 and 29900.
Despite all the bankruptcies and negativity around US regulations, it's better to go long than short, as everything else seems quite positive. Although there are some potential negative catalysts for crypto, and 2023-2024 could be like 2019-2020 for crypto, I think that dips are for buying and that it's more likely than not that we are in a bull market rather than a bear market.
🔥 FET Bullish Reversal Trade: Patience!FET has been trading bearish for months. As of a couple of days ago, BTC saw a huge break out which will likely take alts with it. This trade assumes that FET, an early 2023 winner, will move up together with BTC.
I'm waiting for the break out through the top diagonal resistance. Once a daily candle has closed above said resistance, we're entering from around that level. Target at the 2023 top for the highest risk-reward. If you're more risk averse, consider taking (partial) profits around 0.30 or 0.40
A reverse Adam-Eve idea PLTR has risen with the earnings and AI mania. It is time for a correction for further upside. The price action has started to develop a reverse Adam-Eve pattern. RSI and OBV show some bearish divergence as well. We may see a correction to 0.236 or 0.382 fib retracement levels.
Disclaimer – WhaleGambit. Please be reminded – you alone are responsible for your trading – both gains and losses. There is a very high degree of risk involved in trading. The technical analysis , like all indicators, strategies, columns, articles and other features accessible on/though this site is for informational purposes only and should not be construed as investment advice by you. Your use of the technical analysis , as would also your use of all mentioned indicators, strategies, columns, articles and all other features, is entirely at your own risk and it is your sole responsibility to evaluate the accuracy, completeness and usefulness (including suitability) of the information. You should assess the risk of any trade with your financial adviser and make your own independent decision(s) regarding any tradable products which may be the subject matter of the technical analysis or any of the said indicators, strategies, columns, articles and all other features.
Mitigate Nvidia risk with a value-chain exposure to AIThe recent earnings announcement from Nvidia was historic. It’s not often that a firm shifts revenue guidance for an upcoming quarter from $7 billion to $11 billion. Nvidia’s total market capitalisation touched $1 trillion, something very few companies ever achieve1.
An overzealous valuation?
Professor Aswath Damodaran of New York University2, well known for his work on valuation, has said he cannot rationalise a $1 trillion valuation.
Damodaran estimates Nvidia has a roughly 80% share of the artificial intelligence (AI) semiconductor market, which is around $25 billion today. Using bullish assumptions, which may not prove accurate, he looks to see growth in the AI semiconductor market to reach $350 billion within a decade. If Nvidia captured 100% future market share (a bold assumption), Damodaran’s valuation still resides about 20% below current prices.
Nvidia is essentially a hardware company. One can see them try to ramp up software, but that is not the main driver. Other companies that achieved the $1 trillion market capitalisation level have software companies with network effects that draw vast numbers of end users into ecosystems. These software businesses have many ways to earn revenue from new products and services.
Professor Damodaran’s valuations do not necessarily lead to share prices that immediately decline—but it may be difficult to keep the return momentum coming with equal fervor.
Nvidia’s products do not operate in a vacuum
WisdomTree spends a lot of time focusing on the AI megatrend. Nvidia’s products do not exist in a standalone fashion, as they are plugged into cabinets containing other hardware functioning in concert. If the AI semiconductor market grows, as many now expect, a lot of companies will benefit.
Nvidia cannot, by itself, manufacture its semiconductors end-to-end. Taiwan Semiconductor Manufacturing Co. (TSMC) is responsible for this part of the puzzle. There is a whole semiconductor value chain, and each element captures a different-sized slice of the economic value pie.
There are a range of companies associated with ‘generative AI’ over the period from the release of ChatGPT.
Alphabet, Meta and Microsoft represent companies developing large language models (LLMs) to allow users to directly access generative AI. Meta was beaten down in 2022, due to disappointment with the firm’s metaverse efforts, but AI and cost cutting is helping them in 2023. Alphabet and Microsoft are at the centre of the generative AI battleground. Microsoft, so far, is winning on the cloud computing battle front with its Azure platform, whereas Alphabet’s Google is going to be very difficult to fend off in the internet search space.
It’s interesting to compare Nvidia to Samsung and SK Hynix. Running AI models, especially large AI models, requires memory, and Samsung and SK Hynix are in the memory chip space. Excitement, at least in recent years, fluctuated in waves across the broad semiconductors market. Right now, during the explosion of generative AI, graphics processing units (GPUs), where Nvidia is the leader, are all the rage.
Synopsys and TSMC represent notable, necessary value-chain plays on semiconductors. Nvidia chips cannot be created in a vacuum. Synopsys provides necessary electronic design automation capabilities, whereas TSMC is among the only companies with a manufacturing process advanced enough to fabricate Nvidia’s most advanced chips.
Is AI over-hyped?
The Gartner Hype cycle characterises one way to view new technologies. In the short term, excitement leads to money flows. Share prices and valuations benefit. At a certain point, a realisation sets in that true success, growth, and adoption takes time, so at this point there is usually a lot of selling and a tougher return environment.
Finally, there is a recognition that pessimism is also not quite appropriate as the technology is still important and still being used, so growth rates and returns then tend to be more reasonable.
AI is not any one single thing. Today we think of it as ChatGPT, LLMs or generative AI, but other disciplines and functionalities are still there, they just aren’t grabbing headlines in same way.
‘Generative AI’ and ‘foundation models’ might be nearing a peak of inflated expectations.
Have you been excited about self-driving vehicles recently? No? Well, that could be part of the reason why ‘autonomous vehicles’ might be near the trough of disillusionment.
Computer vision, which has been around for quite some time, is making its way up the so-called ‘slope of enlightenment’.
The hype cycle is not an exact science. Any discipline on this graph could generate any sort of return, positive or negative, going forward. It’s simply a tool that helps us place all of these different topics on a broader continuum. The only thing we seem to know for sure is that all of the topics do not generate the same levels of excitement or pessimism all the time.
Conclusion: it’s possible to mitigate single company risk by looking across the AI ecosystem
The hype cycle illustration points out that the various applications of AI are at different points of adoption, excitement, and development. No one knows the future with certainty, but we believe there is growth occurring in all of these disciplines. The world is enthralled with generative AI now, but the world was similarly excited about autonomous vehicles a few years ago. Progress is occurring, even if we are not seeing it reflected in every headline.
WisdomTree has a broad-based AI index to capture these AI trends. While Nvidia’s valuation is getting stretched, according to Professor Damodaran, WisdomTree’s AI index did not change much following the Nvidia surge. The entire ecosystem of AI defined by WisdomTree is not as beholden to the moves of any single company.
AI has the potential to impact every industry which is why WisdomTree built a broad-based, ecosystem-oriented approach as opposed to concentrating on any single stock.
Sources
1 Source: Bloomberg.
2 Source: Hough, Jack. “Nvidia Is the New Tesla, the ‘Dean of Valuation’ Says. It’s Time to Cash Out.” Barrons. May 31, 2023.
We Called The ETH/BTC Pump!Looking at our chart, we see that Ethereum started getting oversold near the bottom of our standard deviation bands, along with an oversold reading on our new DVO indicator. This, combined with the oversold green X's we received (combination of multiple indicators), and our dark blue candles (another oversold indication), led us to have a VERY successful ETH long!
The next resistance we're looking at is $1915. This has been a big level on the daily chart and may provide a rejection once reached. You can also see our candles starting to get overbought (turning orange). Once they turn red, that's when I start looking to completely exit or take a majority of my profits.
If you're looking to buy on a pullback, watch the $1715 level. That acted as great support over the past few weeks and could provide a good R/R if we reverse here.
With BlackRock recently applying for a Bitcoin ETF and many other banks following suit, the crypto market is about to get a whole lot crazier!
-Stayed tuned for our new indicators launching soon (shown on the chart + more), along with a slew of great trading info for you guys! You won't want to miss it :)
Let us know if you have any questions!
S&P 500: Expensive but Not OverpricedCME: E-Mini S&P 500 ( CME_MINI:ES1! ), S&P Technology Sector ( CME_MINI:XAK1! )
These days, the S&P 500 is not behaving like a well-diversified stock market index. The “Magnificent Seven”, which includes Nvidia NASDAQ:NVDA , Apple NASDAQ:AAPL , Tesla NASDAQ:TSLA , Microsoft NASDAQ:MSFT , Google NASDAQ:GOOGL , Meta NASDAQ:META and Amazon NASDAQ:AMZN , is up roughly 60% year-to-date. These 7 tech stocks now represents ~30% of the entire S&P 500 index.
Meanwhile, the remaining 493 companies in the S&P 500 are up only 3% YTD. Altogether, the S&P 500 is up 15.8% YTD as of June 15th.
The tech-heavy Nasdaq 100, which includes all the Magnificent Seven, is up 39.5% YTD. The Dow Jones Industrial Average, with only one of the seven, NASDAQ:AAPL , in its components, had a very disappointing return of 4.0% YTD.
What sparks the recent market rally is OpenAI’s ChatGPT. Its November 20th launch ignited a global sensation in Artificial Intelligence. By now, the entire US stock market is being held up by the red-hot AI momentum.
S&P 500 Performance by Sector
Of the 11 S&P select sectors, I found that only Technology has a decent 12-month performance. Three other sectors have low single-digit return, and the rest are in the red. (Data source: S&P Global, 12-month returns as of May 31st, 2023).
• Consumer Discretionary: -0.83%
• Consumer Staples: 0.22%
• Energy: -8.23%
• Financials: -8.55%
• Real Estate: -15.47%
• Health Care: -1.71%
• Industrials: -4.15%
• Materials: -10.69%
• Technology: 18.16%
• Utilities: --9.96%
• Communication Services: 4.47%
• S&P 500: 1.15%
Once again, data confirms that the recent stock market rally is exclusively reserved for the tech stocks. Investing in the S&P 500 is like holding an outstanding tech-sector fund on one hand, and a sucker fund of poorly-performing stocks on the other.
Statistical Analysis of the S&P 500
Diving deeper into the S&P, I found that its 3-year mean is 4027.2 as of June 15th. The standard deviation during this period is 395.6. We know from probability distribution that the time series of price data falls inside plus or minus one standard deviation approximately 33% of the time. This corresponds to the index range of 3632 and 4423.
Data trend shows that whenever the index broke away from this boundary, it had the tendency of getting pulled back in. This fits the rule of mean reversion, as seen below:
• The S&P broke through 4400 in August 2021 and reached its record height at 4800 in January 2022. Over the next four months, it plunged 1,000 points, or -22.8%.
• The S&P fell below 3600 in September 2022. It rebounded after it crossed the -1 STD line and regained 24% as of last Friday.
S&P 500 closed at 4,453.75 on June 15th, which placed it 30 points above the +1 STD line. It is approaching “expensive” level from the historical perspective. But will it trend down from here? I seriously doubt it.
The AI momentum could carry the stock market index much higher. We are at an early stage to even access how AI could revolutionize our world. Waves of technological breakthroughs and new applications would continue to fire up investor sentiment.
Recent resolution of the Debt Ceiling Crisis and the Fed pausing rate hikes in June are also strong tailwinds which have helped lift stock market valuation.
If the index reaches the +2 STD line, at 4818.43, we could argue that it marks a turning point. We shall understand that this is not a broad-based stock market rally. The consequence of high inflation and high interest rates would weigh on company profitability for many months to come. At lofty valuation, the Magnificent Seven could no longer carry the weight of 493 mediocre companies. The S&P could come crushing down under its own weight.
Hedging the Risk of a Tech Sector Fallout
In my opinion, while the S&P 500 is expensive, it is not yet overpriced. We could still ride the AI wave by holding stocks or a long position in the stock index futures. I am not particularly concerned whether you call this a new bull market or a bear market rally.
However, the entire stock market is overly concentrated in the tech sector. A handful of chip manufacturers, namely Nvidia and TSMC, holds systemic risk. If their production is threatened by geopolitical conflicts, the entire stock market could crash.
Nvidia sees its share price doubled this year, and has a ridiculous price earnings ratio of 222. Its massive $1 trillion market valuation has been built upon the huge promise of AI. Any negative news on Nvidia would have a disproportionally large impact on the S&P.
To hedge the risk of AI bubble going busted, I am exploring a spread trade with long S&P index futures NYSE:ES and short Technology Select Sector futures $XAK.
Since the Magnificent Seven accounts for 30% of S&P 500 market value, I am considering a 10:3 spread ratio. By measure of contract notional value, for every $100,000 in ES long positions, short XAK by $30,000.
• ESU3 is quoted 4,459 on June 15th. Its notional value is five times the index, or $222,950. Each contract requires a margin of $11,200;
• XAKU3 is quoted 1761.40 on the same day. Its notional value is 100 times the index, or $176,140. Each contract requires a margin of $9,500.
• The spread trade would consist of 4 long ES futures and 1 short XAK futures.
If an investor already had investment in S&P component stocks, he could hold on to them. However, the investor could consider shorting XAK futures to hedge the downside risk.
For every $600K in stock investment, hedge it with 1 short XAK position. The logic of this trade is that if the tech sector gets into trouble, the short XAK trade would protect the value of long stock positions.
Happy trading.
Disclaimers
*Trade ideas cited above are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management under the market scenarios being discussed. They shall not be construed as investment recommendations or advice. Nor are they used to promote any specific products, or services.
CME Real-time Market Data help identify trading set-ups and express my market views. If you have futures in your trading portfolio, you can check out on CME Group data plans available that suit your trading needs www.tradingview.com
The environmental impact of AI: a case studyIn our previous blog, Will AI workloads consume all the world’s energy?, we looked at the relationship between increasing processing power and an increase in energy demand, and what this means for artificial intelligence (AI) from an environmental standpoint. In this latest blog, we aim to further illuminate this discussion with a case study of the world’s biggest large language model (LLM), BLOOM.
Case study on environmental impact: BLOOM
An accurate estimate of the environmental impact of an LLM being run is far from a simple exercise. One must understand, first, that there is a general ‘model life cycle.’ Broadly, the model life cycle could be thought of as three phases1:
Inference: This is the phase when a given model is said to be ‘up-and-running.’ If one is thinking of Google’s machine translation system, for example, inference is happening when the system is providing translations for users. The energy usage for any single request is small, but if the overall system is processing 100 billion words per day, the overall energy usage could still be quite large.
Training: This is the phase when the parameters of a model have been set and the system is exposed to data from which it is able to learn such that outputs in the inference phase are judged to be ‘accurate’. There are cases where the greenhouse gas emissions impact for training large, cutting-edge models can be comparable to the lifetime emissions of a car.
Model development: This is the phase when developers and researchers are seeking to build the model and will tend to experiment with all sorts of different options. It is easier to measure the impact of training a finished model that becomes public, as opposed to seeking to measure the impact of the research and development process, which might have included many different paths prior to getting to the finished model that the public actually sees.
Therefore, the BLOOM case study focuses on the impact from training the model.
BLOOM is trained on 1.6 terabytes of data in 46 natural languages and 13 programming languages.
Note, at the time of the study, Nvidia did not disclose the carbon intensity of this specific chip, so the researchers needed to compile data from a close approximate equivalent setup. It’s an important detail to keep in mind, in that an accurate depiction of the carbon impact of training a single model requires a lot of information and, if certain data along the way is not disclosed, there must be more and more estimates and approximations (which will impact the final data).
If AI workloads are always increasing, does that mean carbon emissions are also always increasing2?
Considering all data centres, data transmission networks, and connected devices, it is estimated that there were about 700 million tonnes of carbon dioxide equivalent in 2020, roughly 1.4% of global emissions. About two-thirds of the emissions came from operational energy use. Even if 1.4% is not yet a significant number relative to the world’s total, growth in this area can be fast.
Currently, it is not possible to know exactly how much of this 700 million tonne total comes directly from AI and machine learning. One possible assumption to make, to come to a figure, is that AI and machine learning workloads were occurring almost entirely in hyperscale data centres. These specific data centres contributed roughly 0.1% to 0.2% of greenhouse gas emissions.
Some of the world’s largest firms directly disclose certain statistics to show that they are environmentally conscious. Meta Platforms represents a case in point. If we consider its specific activities:
Overall data centre energy use was increasing 40% per year from 2016.
Overall training activity in machine learning was growing roughly 150% per year.
Overall inference activity was growing 105% per year.
But Meta Platforms’ overall greenhouse gas emissions footprint was down 90% from 2016 due to its renewable energy purchases.
The bottom line is, if companies just increased their compute usage to develop, train and run models—increasing these activities all the time—then it would make sense to surmise that their greenhouse gas emissions would always be rising. However, the world’s biggest companies want to be seen as ‘environmentally conscious’, and they frequently buy renewable energy and even carbon credits. This makes the total picture less clear; whilst there is more AI and it may be more energy intensive in certain respects, if more and more of the energy is coming from renewable sources, then the environmental impact may not increase at anywhere near the same rate.
Conclusion—a fruitful area for ongoing analysis
One of the interesting areas for future analysis will be to gauge the impact of internet search with generative AI versus the current, more standard search process. There are estimates that the carbon footprint of generative AI search could be four or five times higher, but looking solely at this one datapoint could be misleading. For instance, if generative AI search actually saves time or reduces the overall number of searches, in the long run, more efficient generative AI search may help the picture more than it hurts3.
Just as we are currently learning how and where generative AI will help businesses, we are constantly learning more about the environmental impacts.
Sources
1 Source: Kaack et al. “Aligning artificial intelligence with climate change mitigation.” Nature Climate Change. Volume 12, June 2022.
2 Source: Kaack et al., June 2022.
3 Source: Saenko, Kate. “Is generative AI bad for the environment? A computer scientist explains the carbon footprint of ChatGPT and its cousins.” The Conversation. 23 May 2023.
C3.AI: AI = SHORT - wyckoff distribution & bearish divergence1st - Bearish Divergence: RSI & MFI on 1D & 1W chart
2nd - Wyckoff Method: Distribution TR phase C (UTAD TEST) more pronounced in the 4&1H charts.
Analysis:
There seems to be weakness in the stock, and despite the recent highs and uptick in volume the exhaustion can be seen per the TA presented. BUT REMEMBER, the AI craze is still on going and this could be invalidated in minutes if the whales choose to push the price higher.
Targets if you trust the analysis:
Its pretty simple, I use fib retracement levels 21% and 38.2% as targets.
remember to use risk management and positioning!
*THIS IS NOT AN INVESTMENT ADVICE, JUST SHARING MY ANALYSIS AND INTERNAL THOUGHTS TO MYSELF*
FET possible Roadmap until June 2023This is the monthly chart of FET.
As you know, we had AI hype in Jan, and FET is one of the AI coins. The hype has just started, and 2023 will be an excellent year for this narrative.
The best buy zone for this correction would be ~$0.114-$0.183; you can accumulate FET in Q2 for the final run with a target above ~$2 in Q3.
Patience is key here, don't rush to get into the trade, wait patiently, and enter at the "right" time.
Please hit the boost button if you agree.
Thanks