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Part 3 | All 7 Big Tech | QQQ Sp500 Price level Trend Guide- QQQ still doesnt have a hourly downtrend confirming so daily lower high is not set. - SPY weekly bullflag confirm, so far no follow through yet but we ran out of time so it doesnt count as a rejection for me until i see hourly downtrend - TSLA potentially shaping up an equilibrium - NVDA bull break above 420 back into its all time highs sideways chop zone - AMZN fifth rejection from its 131 chop zone still above support though - GOOGL still the weakest only tech in a daily downtrend - MSFT went form daily downtrend to uptrend today - META same as AMZN in a chop zone rejection 5th time from its resistance.
NASDAQ:AMZNShort
by ArcadiaTrading
Part 2 | All 7 Big Tech | QQQ Sp500 Price level Trend Guide- QQQ still doesnt have a hourly downtrend confirming so daily lower high is not set. - SPY weekly bullflag confirm, so far no follow through yet but we ran out of time so it doesnt count as a rejection for me until i see hourly downtrend - TSLA potentially shaping up an equilibrium - NVDA bull break above 420 back into its all time highs sideways chop zone - AMZN fifth rejection from its 131 chop zone still above support though - GOOGL still the weakest only tech in a daily downtrend - MSFT went form daily downtrend to uptrend today - META same as AMZN in a chop zone rejection 5th time from its resistance.
NASDAQ:QQQShort
by ArcadiaTrading
Part 1 | All 7 Big Tech | QQQ Sp500 Price level Trend Guide- QQQ still doesnt have a hourly downtrend confirming so daily lower high is not set. - SPY weekly bullflag confirm, so far no follow through yet but we ran out of time so it doesnt count as a rejection for me until i see hourly downtrend - TSLA potentially shaping up an equilibrium - NVDA bull break above 420 back into its all time highs sideways chop zone - AMZN fifth rejection from its 131 chop zone still above support though - GOOGL still the weakest only tech in a daily downtrend - MSFT went form daily downtrend to uptrend today - META same as AMZN in a chop zone rejection 5th time from its resistance.
NASDAQ:QQQShort
by ArcadiaTrading
1
META completing 8 straight months of gains. How far can it go?META is about to close the month on the 8th straight green candle, which is of course way beyond any rally in the company's history. Having broken even above the 0.786 Fibonacci level and turned the 1month MA50 into Support again, the question on everyone's mind is how far can the market extend this rally. Looking at its short history, all of Meta's rallies didn't stop before the 1month RSI entered the overbough (over 70.00) zone. And the RSI is currently at 61.88, considerably lower than this limit. Of course it can be argued that this time the rally started after the 1month RSI rebounded from the oversold area, the first time in its history. But technically, it appears that the market both technically and fundamentally has what it needs to keep investors interested and most likely won't correct substantially before testing at least the $385 All Time High. Follow us, like the idea and leave a comment below!!
NASDAQ:METALong
by TheCryptagon
1
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
Does META's stock trend shift from rising to fluctuating?Does META's stock trend shift from rising to fluctuating? This chart shows the weekly candle chart of META stocks over the past two years. The top to bottom golden section of August 2021 is superimposed in the figure. As shown in the figure, since the end of October 2022, the META stock has shown an almost consistent upward rebound trend! In the past three weeks, the price of META stock has been in a long short competition around the top to bottom golden split of 1.382 (265)! The next strong pressure for META stocks will be at the opening price of the big negative line at the end of January 2022 (300.68), which is also the starting point for big bearish positions after peaking!
NASDAQ:META
by Think_More
$META Spinning Top DetectedNASDAQ:META Spinning Top detected. Buyers and Sellers not able to decide which direction to send it, however, today it went up. There are other indicators in favor of positive price action, and we have ER in later part of July. This may push the price higher. It could also see a small pull back shortly. I think $286 is a good target and like everyone else when they announce "AI" efforts it may certainly push higher. *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:METALong
by VIN786
Meta -> Rally Not Over YetHello Traders, welcome to this free and educational multi-timeframe technical analysis. On the weekly timeframe you can see that Meta stock started a crazy dump in September of 2021, dropping roughly 80% in a very short period of time but bounced back significantly. You can also see that the recovery started in October of 2022 and from there Meta created a rally of 200% towards the upside and is now approaching resistance at the $300 level from which I do expect a short term rejection away towards the downside. On the daily timeframe you can see that Meta stock is still massively bullish, creating new highs every single day so I am now just waiting for a retest of the previous resistance at the $275 level and then I do expect a final blow-off to retest the $300 resistance zone. Thank you for watching and I will see you tomorrow! You can also check out my previous analysis of this asset:
NASDAQ:METALong
by basictradingtv
30
I'm not that techno-optimistic. I tend to share the view that the tech sector at SPX is pulling the whole S&P company's along with it in many ways. Consideration of the whole SPX for a while loses its meaning, separation is necessary. Let's group a few big horses together and see what's out there. Okay: NASDAQ:AAPL*NASDAQ:NVDA*NASDAQ:GOOGL*NASDAQ:MSFT*NASDAQ:META could be more, but I think that would be quite telling. Oh my God, Carl... 99.2% The last time this overbought was in 2019. And you think these guys will go even higher without a correction?
NASDAQ:AAPL
by averkie_skila
1
META H&S can send it skyrocket to $294, unless the MA100 breaksMETA has arguably been one of the hottest, if not the hottest, stocks of the year. The minor (for its parabolic state) correction since Tuesday has seen it hit the MA100 (1h), which is so far holding. This has completed a Head and Shoulders pattern, which is technically a bearish structure. If the neckline but more importantly the MA50 (1h) breaks, we expect the price to invalidate the bearish signal of the H&S. Trading Plan: 1. Buy if the price closes above the 268.50 neckline and the MA50 (1h). 2. Sell if it closes under the MA100 (1h). Targets: 1. 294 (Fibonacci 2.0). 2. 250 (the MA200 1h). Tips: 1. The RSI (1h) has crossed over the MA trendline. This is a short term signal of bullish strength. Please like, follow and comment!!
NASDAQ:META
by TradingBrokersView
6
When the Dollar Breaks This Supply Zone, It Will Bring Pain!With the stock market already trading near the 2031 fair value target of $434.98, it's a wonder how far out investors are willing to bet on S&P 500 earnings. Apple and Meta found some resistance near their average analyst targets, and now we have to figure out what comes next. For me I see t least a 50% retracement for the S&P 500, which sits around $412 per share. A strong dollar and other potential catalysts from the economic landscape could also lead to SPY falling lower. I have a fair value range between $370 and $400. Take a listen to the Equity Channel Podcast on Apple, Amazon and Spotify for more information on trading and investing.
TVC:DXYLong
by D1Finance