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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.
SPARKS:AIX
by aneekaguptaWTE
1
Oh no! A Classic WTF Animal is that pattern!I've detected the "WTF Animal" is that pattern in NVIDIA. Is it a dinosaur? A dog? An elephant? Something from out of this world? The "WTF Animal" is that pattern has been around for years now, but only now am I just sharing it. The animal I see is a dinosaur, but I also see how it could be a llama or a gazelle of sorts. Perhaps I need to draw it again. I don't have a position in NVIDIA at the moment, but it is a fascinating company to watch as AI takes the center stage in everything we do. I'll wait around for a trade, but for now I am neutral and just watching. Sometimes it's the best thing you can do: just watch. Sit on your hands. I've never made a good trade that is forced and not planned. Many of you can probably relate. Also, never trade because you think have to. The thing is, currently NVIDIA has all of these characteristics - people think they have to get in on the AI hype. That's not true. I must thank the "WTF Animal" is that pattern, because at the end of the day, the confusing chart and drawing, helps me pass time rather than rushing a trade! This pattern is usually detected when a chart looks so outlandish yet interesting, bullish but also bearish, confusing but also intriguing, that all one can see is a strange animal of sorts. Next time you think you're going to rush a trade, just remember the "WTF Animal" is that pattern. It'll save you from taking a trade you never wanted to take in the first place. Before placing that uncertain trade, first go ahead and draw the animal you see. But remember: this is not an easy pattern to spot.
NASDAQ:NVDA
by scheplick
18
Nvidia will be necessary to step out of the relatively high triaNvidia will be necessary to step out of the relatively high triangle This chart shows the weekly candle chart of Nvidia shares over the past year. The graph overlays the high and low points of November 2021 and October 2022, along with the corresponding golden section. As shown in the figure, the high points of Nvidia shares last week and this week were suppressed by 1.382 of the golden section in the figure! Nvidia's stock has accelerated since the middle of May 2023, and obviously the bull momentum has been released! In the future, it will be necessary to step out of the relatively high triangle and organize it before continuing to exert upward force!
NASDAQ:NVDA
by Think_More
Ninja Talks EP 22: 500 Followers!First off thanks for 500 followers, seems people like my Ninja Talks, so I'll keep um coming. In today's episode I want to talk about two types of anger traders go through in the market, one makes you win and one makes you lose. * Anger Numero Uno The first is pure rage, complete emotionality and it's what the majority of traders even seasoned pros know very well. In poker this would be called "tilting", in trading it's the same shiz it's just the catalyst appears different, they see cards we see candlesticks. Anyway back to the rage, quick story; many-o-moons ago I tilted and blew up my entire trading account (which was basically my entire net worth at the time), I screamed and rubbed my face so aggressively I dislocated my jaw! It's still not 100% aligned years later. This is the brutality of giving into the 1st anger, it takes no prisoners and will at any moment dash your emotional AND physical well being 1000mph at the wall until you learn to master it. * Which brings me to the second Anger. The second Anger, if verbalised, would sound something like "That's it! Let's fuc🤬ING go!", it's a "game on" mentality, not tilted but ready - you understand you're down, but your not giving up - you remain calm but awake. I'll give you an example, back in the day I had an MMA fight after not training for two years. Completely out of shape I took the fight on one week's notice lost 15lbs and jumped in there underweight, depleted, injured and weirdly stupidly confident. Round one begins and I'm tired after just 1 minute, the "gentlemen" across from me realising this proceeds to plod forward and tee off on my baldy head and skinny legs, but then something happened - my mind snapped out of it and basically said "Enough! Let's fu🤬ING go!" - I walked forward angry but calm saw his incoming kick grabbed it mid air, diverted it to my right and threw a rear high kick slapping the "gentleman's" temple "CRACKKKK!!!" and down he went, the fight was over just like that. Here's the thing... Understanding the difference between these two angers are a defining factor between winning and losing in the financial markets, yet very few learn from their outputs and instead point the finger outwardly at others, don't be that guy and instead learn to channel anger into determinative action. Make sense Ninja? Channeling rage (especially as a man) is one of our most potent potentialities, but it must be intentful and purposeful and preferably positive if we want to capture it's true essence. Meditate on this Ninja. I'll see you in the next ep! Follow for more.
NASDAQ:NVDAEducation
by NinjaTradingServices
2
Simple Pattern Targets for NVidiaWe had a head and shoulders that made a 1.5x measured move down, where it created an inverted HS that saw a 1x measured move up to resistance. Sitting at resistance now, if we don't soon see a push through it, we could move back down towards the support area marked on the chart. As long as that support area holds (or the neckline below it), we should see a visit back to our previous ATH, possibly a double-top with a slightly higher or slightly lower high. However, if DXY continues to remain below 105ish, it could see a new ATH instead.
NASDAQ:NVDA
by dudebruhwhoa
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.
NASDAQ:NVDA
by aneekaguptaWTE
Peeking into Super SevensIn our previous paper , we outlined how investors can use CME's Micro S&P 500 Futures to hedge beta exposure and extract pure alpha. The paper referenced that the Super Sevens stocks (Amazon, Apple, Google, Meta, Microsoft, Nvidia, and Tesla) will continue to outperform the broader S&P 500 index. Not only do these stocks benefit from passive investing and ESG investing, these firms also have solid fundamentals to back up their gargantuan valuations. Each of the firms in the Super Sevens offer unique value drivers. Each firm is a market leader in its space and has demonstrated resilient earnings capacity and solid growth potential. Still, each also has its own set of risks. Notably, with the Super Sevens the value drivers outweigh the potential risks. AMAZON VALUE DRIVERS • Blistering profits from AWS offering with dominant market share of 33%. • Market dominance in e-commerce and solid supply chain network. • Successful new categories: Kindle (publishing), Alexa (voice assistant), and Prime (video streaming). POTENTIAL RISKS • Heavy reliance on AWS for profits. Slowing growth in AWS due to slowdown in corporate IT spending. • Low profit margins in e-commerce business. Slowing growth due to lower consumer spending. • Rising competition in cloud services and e-commerce. ANALYST PRICE TARGETS • Across 54 analysts providing a 12-month price target, 42 (77%) having a strong buy rating, 7 (13%) of them have a buy rating, 4 (7%) suggest a hold, while just 1 (2%) has a strong sell rating. • Average 12-month price target stands at 137, with a maximum of 220 and a minimum of 85. TECHNICAL SIGNALS • Technical signals point to momentum deeply in favour of Amazon shares. Oscillators point to buy and Moving averages point to a strong buy. • In aggregate, technical signals point to a buy. APPLE VALUE DRIVERS • Product category definers. Dominant and still growing iPhone demand. • Solid eco-system which is extremely hard to displace. • Control over both software and hardware enables specialized tailored improvements. • Sticky services such as App store, Apple Pay, and potentially Apple BNPL. POTENTIAL RISKS • Apple is heavily reliant on external fabricators exposing it to supply-chain bottlenecks. • Heavily dependent on iPhone sales. • Rising dependence on future growth in unexplored new categories. ANALYST PRICE TARGETS • Across 42 analysts providing a 12-month price target, 22 (52%) having a strong buy rating, 6 (14%) of them have a buy rating, 13 (31%) suggest a hold, while just 1 (2%) has a strong sell rating. • Average 12-month price target stands at 187, with a maximum of 220 and a minimum of 140. TECHNICAL SIGNALS • Technical signals point to solid momentum favouring long position in Apple shares. Oscillators point to buy and Moving averages point to a strong buy. • In aggregate, technical signals point to a strong buy despite Apple trading at near its all-time-high. GOOGLE VALUE DRIVERS • Google is the dominant search engine (86% market share). • Phenomenally successful and effective ad-targeting capabilities. • Heavy investments in future innovation enabling leapfrog into new verticals such as Android, Waymo (FSD & Maps). • Successful early acquisitions such as YouTube, Android, Applied Semantics & DoubleClick (AdSense), Nest (Home Automation). POTENTIAL RISKS • Massive reliance on ad revenues via search for profits. Slowing ad spend as firms cut back on spending. • Non-trivial dependence on cloud revenue for growth exposes them. Slowing cloud revenue growth due to lower corporate IT spending. • Failure to expand into new domains such as social media, wearable tech, and gaming. ANALYST PRICE TARGETS • Across 52 analysts providing a 12-month price target, 40 (77%) having a strong buy rating, 7 (13%) of them have a buy rating, while 5 (10%) suggest a hold. None of the analysts have a sell rating. • Average 12-month price target stands at 131, with a maximum of 190 and a minimum of 100. TECHNICAL SIGNALS • Technical signals point to decent momentum favouring Google shares but prices are at tiny risk of oscillating downwards. Oscillators point to neutral while Moving averages point to a strong buy. • In aggregate, technical signals point to a buy. META VALUE DRIVERS • Market monopoly on social media with high penetration across global markets on multiple platforms. • Flagship Facebook platform continues to see growth with 2.9 billion monthly active users (MAU). • Successful acquisitions have provided them with a wide suite of social media platforms – WhatsApp (2 billion MAU) and Instagram (2 billion MAU). • Successful developer tools (Graph, Hydra, React) have allowed them to build useful SDK (Software Development Kit). Potential sources of enterprise revenue from these. POTENTIAL RISKS • Increasing competition from TikTok. • Privacy concerns have a direct revenue impact e.g., Apple’s new privacy policies. • Falling market share for flagship Facebook in advanced economies. • High reliance on ad-sales. Slowing ad sales as firms cut back on spending. • Shaky bet on the Metaverse which is starting to fade. ANALYST PRICE TARGETS • Across 60 analysts providing a 12-month price target, 39 (65%) having a strong buy rating, 7 (12%) of them have a buy rating, 10 (17%) suggest a hold, 1 (2%) sell rating, and 3 (5%) has a strong sell rating. • Average 12-month price target stands at 281, with a maximum of 350 and a minimum of 100. TECHNICAL SIGNALS • Technical signals point to decent momentum favouring Meta shares. Oscillators signal neutral indicating a tiny risk of shares shedding gains while Moving averages point to a strong buy. • In aggregate, technical signals point to a buy. MICROSOFT VALUE DRIVERS • Sheer dominance of Windows (74% market share) & MS Office. • Deep roots in MS Office enables the firm to straddle across consumers & enterprise. • Diversified software offerings - cloud (Azure), gaming (Xbox), enterprise (Windows Server and SQL), search (Bing), productivity (Office), collaboration (Teams), and AI (through Open AI's ChatGPT). • Active M&A activity to acquire assets - LinkedIn, OpenAI, GitHub, Skype, Mojang, Nokia, Activision-Blizzard (Pending). • Besides Windows, Microsoft controls dev frameworks such as .Net further strengthening their grasp on SW dev. POTENTIAL RISKS • Limited success in hardware offerings unlike Apple. • Multiple major acquisitions have fizzled – Skype and Nokia. • Limited adoption in enterprise software. ANALYST PRICE TARGETS • Across 51 analysts providing a 12-month price target, 37 (73%) having a strong buy rating, 6 (12%) of them have a buy rating, 7 (14%) suggest a hold, while just 1 (2%) has a strong sell rating. • Average 12-month price target stands at 345, with a maximum of 450 and a minimum of 232. TECHNICAL SIGNALS • Technical signals point to decent momentum favouring Microsoft shares. Oscillators are at neutral while Moving averages signal a strong buy. • In aggregate, technical signals point to a strong buy. NVIDIA VALUE DRIVERS • Market dominance in discrete GPU’s (80%). • Early mover in AI hardware which gives them a lead over the competition. • Raytracing, DLSS, Neural Network cores. • Nvidia’s CUDA is the primary choice for training ML models. • Market dominance in high-growth data centre graphics hardware (95%) and super-computing hardware. • Successful enterprise partnerships – car manufacturers using Nvidia software. • Emerging tech such as AI and VR require more graphics intensive processing driving demand for Nvidia’s products. POTENTIAL RISKS • Hardware-focused business model exposes it to supply-chain risks and bottlenecks. • Extremely high P/E of 225 dependent upon expectations of future growth in AI. • Losing market share in discrete GPUs and enterprise GPUs to AMD and Intel. ANALYST PRICE TARGETS • Across 50 analysts providing a 12-month price target, 36 (72%) having a strong buy rating, 6 (12%) of them have a buy rating, 7 (14%) suggest a hold, while just 1 (2%) has a sell rating. • Average 12-month price target stands at 444, with a maximum of 600 and a minimum of 175. TECHNICAL SIGNALS • Technical signals point to solid momentum favouring long position Nvidia shares. Oscillators point to buy and Moving averages point to a strong buy. • In aggregate, technical signals point to a strong buy despite Nvidia relentless and unrivalled price ascent. TESLA VALUE DRIVERS • Early mover in EV’s with dominant market share in US (62%). • Dedicated and loyal customer base. • Vertical integration of EV value chain allows it to reduce reliance on external suppliers. • Early investment in large factories that will allow them to scale output more efficiently. • Huge and monetizable supercharger network by opening it up to other EV makers. • Subscription model for software enables revenue generation after product sale. • Long term vision has allowed Tesla to create entirely new products such as supercharger network, battery banks, home power backup and solar roofs. • Tesla’s planned Robotaxi and entry into car insurance can be hugely disruptive. POTENTIAL RISKS • Increasing competition from automobile majors as well as Chinese EV firms. • Tesla’s brand is deeply entangled with Musk’s reputation. • Dependence on government incentives to make Tesla affordable. • Continued access to battery metal minerals. • Ongoing and unresolved production scaling challenges. ANALYST PRICE TARGETS • Across 46 analysts providing a 12-month price target, 18 (39%) having a strong buy rating, 5 (11%) of them have a buy rating, 17 (37%) suggest a hold, 1 (2%) has a sell rating, and a 5 (11%) hold a strong sell rating. • Average 12-month price target stands at 201, with a maximum of 335 and a minimum of 71. TECHNICAL SIGNALS • Technical signals point to solid momentum favouring Tesla. Oscillators point to buy and Moving averages point to a strong buy. • In aggregate, technical signals point to a strong buy. SUMMARY The Super Sevens are well positioned to continue outperforming the wider market. As mentioned in our previous paper , investors can use a beta hedge to nullify the effects of the broader market (S&P 500) and extract pure alpha from the growth of the Super Sevens. MARKET DATA CME Real-time Market Data helps identify trading set-ups and express market views better. 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 DISCLAIMER This case study is for educational purposes only and does not constitute investment recommendations or advice. Nor are they used to promote any specific products, or services. Trading or investment ideas cited here are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management or trading under the market scenarios being discussed. Please read the FULL DISCLAIMER the link to which is provided in our profile description.
NASDAQ:AAPLLong
by mintdotfinance
35
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.
SPARKS:AIX
by aneekaguptaWTE
Harvesting Alpha with Beta HedgingImagine this. Dark skies, earth tremors and thunder roars. Shelter is top priority. Size matters in a crisis. When the tsunami strikes and lightning splits the sky, investors shudder in fear; But the super seven stand tall, shielding investors from the fury. Dramatic metaphors aside, we truly live in unprecedented times. Risk lurks everywhere. List is endless. Unstable geopolitics. Sticky inflation. Recession expectations. Unprecedented deepening of yield curve inversion. Unfinished regional banking crisis. Weak manufacturing. Tightening financial conditions. Extremely divisive global politics, to just name a few. Despite severe headwinds, US equity markets are roaring. YTD, S&P is up +15% and Nasdaq is up +32%. At the start of 2023, the consensus was for US equities to be in doldrums dragged down by recession. Halfway through the year, markets are at the cusp of one of the best first half for US equity markets in twenty years. This is among the narrowest and top-heavy rally ever. Only a sliver of stocks - precisely seven of them - defines this optimism. This paper will refer to these as the Super Sevens. These are the biggest members of the S&P 500 index. Super Sevens are Amazon, Apple, Google, Meta, Microsoft, Nvidia, and Tesla. This paper argues that the Super Sevens will deliver above market returns in the short term as investors seek safe haven from a vast array of macro risks. The paper articulates a case study to demonstrate the use of beta hedging to extract alpha from holding long positions in Super Sevens and hedging them against sharp reversals using CME Micro E-Mini S&P 500 index futures ("CME Micro S&P 500 Futures"). THE RISE AND RISE OF SUPER SEVENS Super Sevens have an outsized impact as S&P 500 is a market weighted index. Merely five of these seven form 25% of the S&P 500 market capitalisation. At $2.9 trillion in market capitalisation, Apple is greater than all of UK’s top 100 listed companies put together. If that were not enough, Apple's market capitalisation alone is greater than the aggregate market capitalisation of all the firms in the Russell 2000 index. Nvidia has been soaring on hopes of AI driven productivity gains. On blow out revenue guidance, it has rallied $640 billion in market cap YTD. That increment alone is larger than the combined market cap of JP Morgan & Bank of America the two largest banks in the US. The heatmap summarises analyst targets & technical signals on pathway for prices ahead: In part 2 of this paper, Mint will cover the detailed analyst price forecasts, technical signals and summary narratives covering value drives and intrinsic risk factors. WHAT DRIVES INVESTOR CONCENTRATION INTO THE SUPER SEVENS? As reported in the Financial Times last week, two broad market trends appear to have fed into this investor concentration. First, Passive investing. When funds merely deliver the performance of an index by replicating its composition, the higher the index weights, the more these passive funds buy into these names. Second, ESG investing. Rising push towards ESG has forced investment into tech and away from carbon-heavy sectors such as energy. Collectively, this has resulted in all types of investors – active, passive, momentum, ESG- all going after the same names. Question is, what happens now? Will the broader market catch up with the Super Sevens? Or will the Super Sevens suffer a sharp pullback? That depends on the broader US economy. Will it have a hard landing, soft landing, or no landing at all? Given market expectations of (a) resilient earnings capacity, and (b) solid growth potential among Super Sevens, we expect that in the near to mid-term the Super Sevens will continue to outperform the broader market. In ordinary times, investors could have simply established long positions in Super Sevens and wait to reap their harvests. However, we live in unprecedented times. WE LIVE IN TRULY UNPRECEDENTED TIMES Risks abound but no signs of it in equity markets. Historically, geopolitical instability, tightening financial conditions, and a deeply inverted curve could have led to crushing returns in the US equity markets. Not this time though. Peak concentration As mentioned earlier, bullishness in equity markets can be vastly attributed to just the Super Sevens. These seven have delivered crushing returns rising between 40% and 192% YTD. The S&P 500 index is market cap weighted. Super Sevens represent the largest companies in the index by market cap and their stellar performance has an outsized impact on the index. Is this a bull run or a bear market clouded by over optimism among Super Sevens? Deeply inverted yield curve In simple words, it costs far more to borrow for the near term (2 year) relative to the borrowing for long term (10-year). The US Treasury yield curves have been inverted for more than a year now. The difference between the 2-Year and 10-Year treasuries is at its widest level since the early 1980s. Inversion in yield curve has historically been a credible signal of recession ahead. When bonds with near term duration yield higher rates than those with longer-dated expiries, this precedes trouble in the economy. Recession. What recession? This period might go into the record books for the most long-awaited recession that is yet to come. For the last 12 months, experts have been calling for recession to show up in 3 months. While manufacturing sector seems feeble, labour market remains solid. Corporate balance sheets are robust. Consumer finances and consumer confidence are in good health. The VIX remains sanguine while the only fear indicator that appears unsettled is the MOVE index which indicates volatility in the bond markets. After having spiked earlier in the year, the MOVE is starting to soften as well. BETA HEDGING FOR PURE ALPHA In times of turbulence, risk management is not an afterthought but a necessity. Hedge delivers the edge. When there are ample arguments to be made for bullish and bearish markets, taking a directional position can be precarious. This paper posits Super Sevens holdings be hedged with CME Micro S&P 500 Futures. Hedging single stocks is nuanced. The stocks and the index do not always move in tandem. A given stock may be more volatile or less volatile relative to the benchmark. Beta is the sensitivity of the stock price relative to a benchmark. Beta is computed from daily returns over a defined historical period. Stocks with high Beta move a lot more than the underlying index. Stocks that move narrowly relative to its underlying benchmark exhibits low Beta. Beta hedging involves adjusting the notional value of a stock price based on its beta. Using beta-adjusted notional, hedging then involves taking an offsetting position in an index derivative contract to match the notional value. TradingView publishes beta values computed based on daily returns over the last 12 months. The following table illustrates the beta-adjusted notional for the Super Sevens based on the last traded prices as of close of market on June 16th. Beta hedging using CME Micro S&P 500 Futures enables investors to precisely scale their portfolio exposures to the index. A small contract size enables investors to manage risks with finer granularity. CME allows conversion of micro futures into a classic E-mini futures position, and vice versa. Round the clock liquidity combined with tight spreads and sizeable open interest across the two front contract months, investors can enter and exit the market at ease. BETA-HEDGED TRADE SET UP In unprecedented times like today, markets may continue to rally or come crashing. To harness pure alpha, this paper posits a spread with long positions in Super Sevens hedged by a short position in CME Micro S&P 500 Futures expiring in September 2023. This trade set-up gains when (a) Super Sevens rise faster than the S&P 500, or (b) Super Sevens suffers drop in value but falls lesser relative to S&P 500, or (c) Super Sevens gain while S&P 500 falls. This trade setup loses when (a) Super Seven falls faster than S&P 500, or (b) S&P 500 rises faster than Super Seven, or (c) S&P 500 rises while Super Sevens pullback Each CME Micro S&P 500 Futures has a multiplier of USD 5. The September contract settled on June 16th at 4453.75 implying a notional value of USD 22,269 (4453.75 * USD 5). Effective beta hedge requires that notional of the hedging trade is equivalent to the beta-adjusted notional value of single stock. Given the beta-adjusted notional value of USD 2,561 for single shares in Super Sevens and the notional value for each lot of CME Micro S&P 500 Futures at USD 22,269, the spread trade requires: a. A long position in 26 shares each across all the Super Sevens translating to a beta-adjusted notional of USD 66,576. b. Hedged by a short position with 3 lots of CME Micro S&P 500 Futures which provides a notional exposure of USD 66,807. The following table illustrates the hypothetical P&L of this spread trade under various scenarios: MARKET DATA CME Real-time Market Data helps identify trading set-ups and express market views better. 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 DISCLAIMER This case study is for educational purposes only and does not constitute investment recommendations or advice. Nor are they used to promote any specific products, or services. Trading or investment ideas cited here are for illustration only, as an integral part of a case study to demonstrate the fundamental concepts in risk management or trading under the market scenarios being discussed. Please read the FULL DISCLAIMER the link to which is provided in our profile description.
NASDAQ:AAPLLong
by mintdotfinance
3
$NVDA headed for a reversalNASDAQ:NVDA headed for a reversal. Looking at candlestick patterns on 15th June 2023, NVDA is losing steam and trend reversal is suggested. Some other indicators are also bearish. The near term resistance levels I see are $438.14 | $446.31 | $462.62 By the end of August 2023 I would not be surprised to see NVDA trading at or below $350 *Disclaimer* The information is purely for *entertainment* purposes, and is not meant to be, and does not constitute, financial, investment, trading, or other types of advice or recommendations. Do Your Own Due Diligence (DYODD)
NASDAQ:NVDA
by VIN786
Harnessing the AI Revolution: A Powerful Surge with NVIDIA, GoogThe future is now, and it's coded in the language of Artificial Intelligence. As investors, we have a unique opportunity to be part of this game-changing journey. My personal story began with NVIDIA, an industry leader in AI and graphics processing. Acquiring NVIDIA shares two months ago was akin to boarding a spacecraft destined for new frontiers. The ride has been exceptional, with returns exceeding my expectations. But, the vast landscape of AI is not limited to one planet. There's a whole universe to explore, and I decided to broaden my horizons. Hence, I ventured further, incorporating three other stellar entities into my portfolio - Google, Microsoft, and IBM. These industry titans are carving their paths, harnessing AI to innovate, and influencing global trends. My portfolio is not just an investment; it's a belief in a future shaped by AI, a testament to a revolution unfolding right before our eyes. Join me in this journey, as I share my insights, strategies, and perspectives on navigating these high-tech tides. Together, we can capitalize on the industry that is relentlessly and rapidly shaping our tomorrow. Remember, the revolution might be digitized, but the rewards are very real.
NASDAQ:NVDALong
by Titans_Trading
NVIDIA - A Scenario Few Are Considering. Few. Few. Few.NVIDIA's price action last week was a historic event in the markets, and at a very strange time. Whenever you see such an outlier, it's time to perk up and really give a deep think to what's going on in the world at large. For me, I had long since anticipated NVIDIA would print a new ATH, but I did not believe it would do it until the markets at large had started to moon, which I stated in a March call, which turned out to be pretty accurate. NVDIA - Expect Sideways Until Bear Puts Expire Worthless The fact that a megacap could take out the November of 2021 highs before the Fed started hiking is extremely indicative of what's going on, namely that the indexes and the market at large are sure to follow. I've heard some pretty good theories that NVIDIA being able to do what it's done has a lot to do with Chinese Communist Party entities running a "boomerang" through Cayman Islands-based proxies that are shuffling liquidity through big enterprises like the US banks located in Hong Kong. NVIDIA also reportedly relies on Taiwan-based TSMC to make its processors, and right now, Taiwan is the springboard for the western globalist interests to attempt to take control of Mainland China when the CCP collapses in the upcoming future. The Party has recently stated that the mainland is scheduled to get hit up by 60 million new cases ***per week*** of the nouveau variant of the Omicron version of the Wuhan-originating Coronavirus Disease, and yet the Communist Party is not reporting any hard figures on case counts and death through the global faucets, and has not since Xi dropped the Zero COVID social credit scheme in January. And on top of that is the soon-to-be 24 year long persecution of Falun Dafa by Jiang Zemin and its Shanghai faction combining with the CCP itself, a persecution that targeted 100 million people and committed the unprecedented sin of live organ harvesting. The sin of the persecution is so enormous that once brought into the public eye, no matter who you are in this world, you'll be brought down as retribution for evil. So there's a lot to watch out for in geopolitical tensions, and a lot at play. The biggest thing right now is that the markets are set to pump to provide people with a new distraction as they try, once again, to get rich, and quick, instead of paying attention to what is important in life. Everyone is now convinced that NVIDIA is unshortable, and some are even looking for a mild pullback to go long on the "parabolic trend line." Frankly speaking, there's a lot of risk in buying ATHs when you're dealing with something governed by a clever MM, and if the Q2 ER scam doesn't convince you that NVIDIA's MM is clever, "Sorry, I don't have time to explain it to you." In making this call, I would like to say that NVIDIA going parabolic is pretty likely. I'd also like to say that some formation like this, which we saw on Sun Microsystems in the Dotcom bubble, is also pretty likely: If the Sun fractal is valid, then this call is invalid. How it would play out is kind of like what Boeing did in 2018-19: Or what BTC CME Futures has already done Meaning that shorting will remain extremely risky, but going long won't necessarily have any opportunities to meaningfully pay. However, if the MMS are intending to conduct a turtle soup into a three drives/three Indians pattern, you do actually have the opportunity to Shortgod the top, get long at the bottom, and collect an even bigger trade. What this would involve is that starting in June NVDIA begins to retrace, and if it were to be so, it would likely retrace with a consistency that is as good as selling volatility has been in the last 9 months. It would refill the May gap completely, and rebalance the unbalanced March gap, which coincides with the recent market structure's range equilibrium at $250 and the week of April 24's pivot. Many have said that the debt ceiling crisis being resolved by the Federal government often results in a stock market crash since the market has to absorb all the new TBonds that the Treasury has to issue to keep the government afloat. If you couple that with how the market didn't go down at all during the debt ceiling crisis itself, a bear impulse appears more and more likely. If it were to do this, NVIDIA would also never print a $1 trillion market capitalization despite being so close. NVDIA likely would quickly bounce at this point and then the target would be one standard deviation above the May high, coming in at $540, which would also take the psychological $500 level. Doing this will encourage and trap bears all the way down, and then slaughter bulls over $500. Doing this will slaughter the bulls that have already bought the top, and at present, the bears have literally all been killed. Projected time frame for this to happen would be something like a September bottom and the top would come in the middle of '24 with the next U.S. Presidential Election on the horizon. Of course, that assumes that the world remains in good enough shape to be stable in any way a year from now. I do not have conviction that this will be the case it will play out, but I wanted to post this theory because the timing, logic, and price action all support it strongly, and it's the one scenario that nobody is considering, which also happens to generate a lot of alpha if you can get on top of it.
NASDAQ:NVDA
by LordWrymouth
6
Artificial intelligence: signs of acceleration in 2023“One final investment area that I’ll mention, that’s core to setting Amazon up to invent in every area of our business for many decades to come, and where we’re investing heavily, is Large Language Models (“LLMs”) and Generative AI. Machine learning has been a technology with high promise for several decades, but it’s only been the last five to ten years that it’s started to be used more pervasively by companies. This shift was driven by several factors, including access to higher volumes of compute capacity at lower prices than was ever available. Amazon has been using machine learning extensively for 25 years, employing it in everything from personalised ecommerce recommendations, to fulfillment center pick paths, to drones for Prime Air, to Alexa, to the many machine learning services AWS offers (where AWS has the broadest machine learning functionality and customer base of any cloud provider). More recently, a newer form of machine learning, called Generative AI, has burst onto the scene and promises to significantly accelerate machine learning adoption.” Amazon.com CEO Andy Jassy1 When Amazon’s CEO makes such a statement, we pay attention. In 1997, Amazon.com had revenues of $147.8 million; in 2022, this figure was $434 billion for Amazon’s consumer business. Amazon Web Services was conceptualised in 2003, with the first services launched in 2006 and, in 2022, generated $80 billion in revenues. Elsewhere, The Stanford AI Index Steering Committee, Institute for Human-Centered AI (one of the best annual resources on artificial intelligence), have also just released a new report. Artificial intelligence (AI) is, undoubtedly, a big topic in 2023, and this report provides an excellent resource for understanding how it is progressing. The full piece is almost 400 pages, but we wanted to highlight some key points. ChatGPT was not the only big AI development of 2022 On November 30, 2022, ChatGPT was launched, but the Stanford AI Index report helps us remember other notable events in 2022. Our 5 favourites: February 16, 2022: DeepMind trained a reinforcement learning agent to control nuclear fusion plasma in a tokamak2. While this doesn’t mean that fusion powerplants are immediately around the corner, it does show a notable use case for AI to help scientific research in a very, very difficult area. April 5, 2022: Google released its PaLM large language model with 540 parameters. This was an important step, showing that one avenue to improve the performance of these models was to simply train them on more data. As of this writing, we do not know how this figure compares to the number of parameters in use for OpenAI’s GPT-4. May 12, 2022: DeepMind showcased Gato, which is a model that can generalise across such activities as: robotic manipulation, game player, image captioning, and natural language generation. June 21, 2022: GitHub makes Copilot available as a subscription-based service for individual developers. Copilot is a generative AI system that can turn natural language prompts into coding suggestions across multiple languages. July 8, 2022: Nvidia uses reinforcement learning to design better-performing GPUs, accelerating the performance of its latest H100 class of GPU chips. Insights on global corporate investment AI has been one of the hottest areas for corporate investment, but Figure 1 shows the total level of investment shifted downwards, from $276.14 billion to a level of $189.59 billion in 2022 with the market volatility. The two biggest categories comprising the level of AI investment recently has been ‘Merger/Acquisition’ and ‘Private Investment.’ Both of these categories dropped significantly from 2021 to 2022, but this is not surprising in that both of these would be expected to slow in a less certain economic environment with the US Federal Reserve quickly raising the cost of capital. One of the most informative charts in the 400-page report is the specific focal areas of investment, and how they have changed. ‘Medical & Healthcare’ was the biggest focal area in 2022, after being second biggest in 2021, trailing only ‘Data Management, Processing and Cloud.’ ‘Cybersecurity, Data Protection’ was the fourth biggest investment area in 2022 and the largest that saw an acceleration in investment, meaning investment in 2022 was actually larger than in 2021. The Russia/Ukraine conflict in 2022 created a big focus on cybersecurity. There is little question, the first four months of 2023 have seen a massive focus on AI, and a massive focus usually leads to at least some hype and some risk of near-term overvaluation. Sometimes this is the nature of thematic investment—we all want something to get excited about, especially if economic growth and geopolitics are less positive. What is emphasised in the letter from Amazon.com CEO, Andy Jassy, and then measured in the 2023 Stanford AI Index report, is that the AI megatrend is continuing to grow and increase in its impact on society and on businesses. Sources 1 Source: aboutamazon andy-jassy-2022-letter-to-shareholders 2 A tokamak, put simply, is somewhat of a doughnut in shape and is a device used to contain the plasma in a fusion reaction.
SPARKS:AIX
by aneekaguptaWTE
14
Nvidia's runaway gap could keep it king of the NasdaqWhilst Meta platforms has closed the gap with Nvidia in terms of YTD performance on the Nasdaq 100, Nvidia remains king of the crop having climbed over 170% from its 2022 low. Prices blew past their previous record high set in 2022, and since consolidated around the current cycle highs. An initial inspection of the higher timeframes suggests it could be 'overbought' - at least over the near-term. But to expect a solid reversal of gains would likely require the combination of a broader market downturn alongside loss of confidence in AI (with the latter feeling unlikely at present). Therefor, a broader market downturn could simply provide the catalyst for a pullback and for AI-bulls to load up at more favourable levels. And if a downturn does not occur? We could be looking at a breakout from its current consolidation. Assuming the recent swing lows hold and prices break higher, it could trigger another bout of technical buying from those who identified the 'runaway gap'. Such gaps tend to appear around the midway point of a strong trend, and mark another round of strong buying as those who missed the first move cannot sit on their hands any longer. And with the AI frenzy unlikely to peter out for some time, perhaps a bullish breakout isn't so crazy (even if the charts suggest it could be overbought by some measures).
NASDAQ:NVDALong
by CityIndex
NVDA - MyMI Options PlaysWe sold our NVDA PUTs that we were carrying since last Friday and purchased 2 separate Calls on 2 different Expiration Dates with the intention of selling the shorter timeframe sooner and using the profits for reversals while the longer-term option played out. I expect this to at least retest the $395 Levels if not $404 by the end of next week if not tomorrow. Due to time decay over the weekend, will have to consider if the profits obtained by the end of the trading session tomorrow (Friday 6/8/23) is worth losing the money over the weekend waiting game + any external variables that may cause the stock's current trend to shift/deviate.
NASDAQ:NVDALong
by MyMIWallet