In my last alert I said that I had sold all my leveraged ETFs to invest more in Nvidia. A subscriber messaged me to say he thought this was a mistake. I can see that it was a drastic step and I want to explain my thinking.
I remain super bullish of leveraged ETFs, including QQQ3, which for most UK investors is the only one available to buy. The chart above, on the brink of giving a double whammy buy signal using 6m candle sticks, is strong and pointing higher.
My problem was two fold. I had to hold my leveraged ETFs, which included TECL and SOXL, in a share account. If they rose, which they have been doing, strongly since I bought them, this produces excellent gains. TECL and SOXL had both more than doubled. But this does not create any margin to buy further shares whereas the same thing happening in a CFD or Spread Betting account means I can build my holdings rapidly.
Secondly, gains in a share account are taxable whereas in a spread betting account they are tax free. So I have moved all my funds to a spread betting account and because I am super bullish of Nvidia that is where I have invested the money.
IG does not allow beginner investors like me to hold leveraged ETFs anywhere but in a share account so there is no avoiding the tax man without doing something like what I have done.
Table of Contents
No question this is a much riskier strategy but I think it is appropriate (a) at this relatively early stage of a new bull market and (b) give the extraordinarily exciting things happening at Nvidia.
Below is a blog (rather lengthy I know) which I have just found on the Nvidia web site setting out in some detail where generative AI could be taking the world with Nvidia cheerleader in chief making it happen.
How Generative AI Could Change the World
A watershed moment on Nov. 22, 2022, was mostly virtual, yet it shook the foundations of nearly every industry on the planet.
On that day, OpenAI released ChatGPT, the most advanced artificial intelligence chatbot ever developed. This set off demand for generative AI applications that help businesses become more efficient, from providing consumers with answers to their questions to accelerating the work of researchers as they seek scientific breakthroughs, and much, much more.
Businesses that previously dabbled in AI are now rushing to adopt and deploy the latest applications. Generative AI — the ability of algorithms to create new text, images, sounds, animations, 3D models and even computer code — is moving at warp speed, transforming the way people work and play.
By employing large language models (LLMs) to handle queries, the technology can dramatically reduce the time people devote to manual tasks like searching for and compiling information.
The stakes are high. AI could contribute more than $15 trillion to the global economy by 2030, according to PwC. And the impact of AI adoption could be greater than the inventions of the internet, mobile broadband and the smartphone — combined.
The engine driving generative AI is accelerated computing. It uses GPUs, DPUs and networking along with CPUs to accelerate applications across science, analytics, engineering, as well as consumer and enterprise use cases.
Click to view the infographic: Generating the Next Wave of AI Transformation
Generative AI for Drug Discovery
Today, radiologists use AI to detect abnormalities in medical images, doctors use it to scan electronic health records to uncover patient insights, and researchers use it to accelerate the discovery of novel drugs.
Traditional drug discovery is a resource-intensive process that can require the synthesis of over 5,000 chemical compounds and yields an average success rate of just 10pc. And it takes more than a decade for most new drug candidates to reach the market.
Researchers are now using generative AI models to read a protein’s amino acid sequence and accurately predict the structure of target proteins in seconds, rather than weeks or months.
Using NVIDIA BioNeMo models, Amgen, a global leader in biotechnology, has slashed the time it takes to customize models for molecule screening and optimization from three months to just a few weeks. This type of trainable foundation model enables scientists to create variants for research into specific diseases, allowing them to develop target treatments for rare conditions.
Whether predicting protein structures or securely training algorithms on large real-world and synthetic datasets, generative AI and accelerated computing are opening new areas of research that can help mitigate the spread of disease, enable personalized medical treatments and boost patient survival rates.
Generative AI for Financial Services
According to a recent NVIDIA survey, the top AI use cases in the financial services industry are customer services and deep analytics, where natural language processing and LLMs are used to better respond to customer inquiries and uncover investment insights. Another common application is in recommender systems that power personalized banking experiences, marketing optimization and investment guidance.
Advanced AI applications have the potential to help the industry better prevent fraud and transform every aspect of banking, from portfolio planning and risk management to compliance and automation.
Eighty percent of business-relevant information is in an unstructured format — primarily text — which makes it a prime candidate for generative AI. Bloomberg News produces 5,000 stories a day related to the financial and investment community. These stories represent a vast trove of unstructured market data that can be used to make timely investment decisions.
NVIDIA, Deutsche Bank, Bloomberg and others are creating LLMs trained on domain-specific and proprietary data to power finance applications.
Financial Transformers, or “FinFormers,” can learn context and understand the meaning of unstructured financial data. They can power Q&A chatbots, summarize and translate financial texts, provide early warning signs of counterparty risk, quickly retrieve data and identify data-quality issues.
These generative AI tools rely on frameworks that can integrate proprietary data into model training and fine-tuning, integrate data curation to prevent bias and use guardrails to keep conversations finance-specific.
Expect fintech startups and large international banks to expand their use of LLMs and generative AI to develop sophisticated virtual assistants to serve internal and external stakeholders, create hyper-personalized customer content, automate document summarization to reduce manual work, and analyze terabytes of public and private data to generate investment insights.
Generative AI for Retail
With 60pc of all shopping journeys starting online and consumers more connected and knowledgeable than ever, AI has become a vital tool to help retailers match shifting expectations and differentiate from a rising tide of competition.
Retailers are using AI to improve customer experiences, power dynamic pricing, create customer segmentation, design personalized recommendations and perform visual search.
Generative AI can support customers and employees at every step through the buyer journey.
With AI models trained on specific brand and product data, they can generate robust product descriptions that improve search engine optimization rankings and help shoppers find the exact product they’re looking for. For example, generative AI can use metatags containing product attributes to generate more comprehensive product descriptions that include various terms like “low sugar” or “gluten free.”
AI virtual assistants can check enterprise resource planning systems and generate customer service messages to inform shoppers about which items are available and when orders will ship, and even assist customers with order change requests.
Fashable, a member of NVIDIA Inception’s global network of technology startups, is using generative AI to create virtual clothing designs, eliminating the need for physical fabric during product development. With the models trained on both proprietary and market data, this reduces the environmental impact of fashion design and helps retailers design clothes according to current market trends and tastes.
Expect retailers to use AI to capture and retain customer attention, deliver superior shopping experiences, and drive revenue by matching shoppers with the right products at the right time.
Whether improving customer service, streamlining network operations and design, supporting field technicians or creating new monetization opportunities, generative AI has the potential to reinvent the telecom industry.
Telcos can train diagnostic AI models with proprietary data on network equipment and services, performance, ticket issues, site surveys and more. These models can accelerate troubleshooting of technical performance issues, recommend network designs, check network configurations for compliance, predict equipment failures, and identify and respond to security threats.
Generative AI applications on handheld devices can support field technicians by scanning equipment and generating virtual tutorials to guide them through repairs. Virtual guides can then be enhanced with augmented reality, enabling technicians to analyze equipment in a 3D immersive environment or call on a remote expert for support.
New revenue opportunities will also open for telcos. With large edge infrastructure and access to vast datasets, telcos around the world are now offering generative AI as a service to enterprise and government customers.
As generative AI advances, expect telecommunications providers to use the technology to optimize network performance, improve customer support, detect security intrusions and enhance maintenance operations.
Generative AI for Energy
In the energy industry, AI is powering predictive maintenance and asset optimization, smart grid management, renewable energy forecasting, grid security and more.
To meet growing data needs across aging infrastructure and new government compliance regulations, energy operators are looking to generative AI.
In the U.S., electric utility companies spend billions of dollars every year to inspect, maintain and upgrade power generation and transmission infrastructure.
Until recently, using vision AI to support inspection required algorithms to be trained on thousands of manually collected and tagged photos of grid assets, with training data constantly updated for new components. Now, generative AI can do the heavy lifting.
With a small set of image training data, algorithms can generate thousands of physically accurate images to train computer vision models that help field technicians identify grid equipment corrosion, breakage, obstructions and even detect wildfires. This type of proactive maintenance enhances grid reliability and resiliency by reducing downtime, while diminishing the need to dispatch teams to the field.
Generative AI can also reduce the need for manual research and analysis. According to McKinsey, employees spend up to 1.8 hours per day searching for information — nearly 20pc of the work week. To increase productivity, energy companies can train LLMs on proprietary data, including meeting notes, SAP records, emails, field best practices and public data such as standard material data sheets.
With this type of knowledge repository connected to an AI chatbot, engineers and data scientists can get instant answers to highly technical questions. For example, a maintenance engineer troubleshooting pitch control issues on a turbine’s hydraulic system could ask a bot: “How should I adjust the hydraulic pressure or flow to rectify pitch control issues on a model turbine from company X?” A properly trained model would deliver specific instructions to the user, who wouldn’t have to look through a bulky manual to find answers.
With AI applications for new system design, customer service and automation, expect generative AI to enhance safety and energy efficiency, as well as reduce operational expenses in the energy industry.
Generative AI for Higher Education and Research
From intelligent tutoring systems to automated essay grading, AI has been employed in education for decades. As universities use AI to improve teacher and student experiences, they’re increasingly dedicating resources to build AI-focused research initiatives.
For example, researchers at the University of Florida have access to one of the world’s fastest supercomputers in academia. They’ve used it to develop GatorTron — a natural language processing model that enables computers to read and interpret medical language in clinical notes that are stored in electronic health records. With a model that understands medical context, AI developers can create numerous medical applications, such as speech-to-text apps that support doctors with automated medical charting.
In Europe, an industry-university collaboration involving the Technical University of Munich is demonstrating that LLMs trained on genomics data can generalize across a plethora of genomic tasks, unlike previous approaches that required specialized models. The genomics LLM is expected to help scientists understand the dynamics of how DNA is translated into RNA and proteins, unlocking new clinical applications that will benefit drug discovery and health.
To conduct this type of groundbreaking research and attract the most motivated students and qualified academic professionals, higher education institutes should consider a whole-university approach to pool budget, plan AI initiatives, and distribute AI resources and benefits across disciplines.
Generative AI for the Public Sector
Today, the biggest opportunity for AI in the public sector is helping public servants to perform their jobs more efficiently and save resources.
These administrative roles often involve time-consuming manual tasks, including drafting, editing and summarizing documents, updating databases, recording expenditures for auditing and compliance, and responding to citizen inquiries.
To control costs and bring greater efficiency to routine job functions, government agencies can use generative AI.
Generative AI’s ability to summarize documents has great potential to boost the productivity of policymakers and staffers, civil servants, procurement officers and contractors. Consider a 756-page report recently released by the National Security Commission on Artificial Intelligence. With reports and legislation often spanning hundreds of pages of dense academic or legal text, AI-powered summaries generated in seconds can quickly break down complex content into plain language, saving the human resources otherwise needed to complete the task.
AI virtual assistants and chatbots powered by LLMs can instantly deliver relevant information to people online, taking the burden off of overstretched staff who work phone banks at agencies like the Treasury Department, IRS and DMV.
With simple text inputs, AI content generation can help public servants create and distribute publications, email correspondence, reports, press releases and public service announcements.
The analytical capabilities of AI can also help process documents to speed the delivery of vital services provided by organizations like Medicare, Medicaid, Veterans Affairs, USPS and the State Department.
Generative AI could be a pivotal tool to help government bodies work within budget constraints, deliver government services more quickly and achieve positive public sentiment.
Generative AI – A Key Ingredient for Business Success
Across every field, organizations are transforming employee productivity, improving products and delivering higher-quality services with generative AI.
To put generative AI into practice, businesses need expansive amounts of data, deep AI expertise and sufficient compute power to deploy and maintain models quickly. Enterprises can fast-track adoption with the NeMo generative AI framework, part of NVIDIA AI Enterprise software, running on DGX Cloud. NVIDIA’s pretrained foundation models offer a simplified approach to building and running customized generative AI solutions for unique business use cases.
Nvidia blog post, 13 July 2023
This is pure marketing for Nvidia but it is also very informative on what is happening and why Nvidia is such a good way to play this explosive new technology trend. It also highlights the way that Nvidia is changing from a semiconductor company into something more software based and much closer to the customer and that is a phenomenally exciting development which may not yet be fully recognised by investors.
In a nutshell I believe Nvidia may be the most exciting company in the world. Two decades ago I believed the same thing about Alphabet/ Google and that proved resoundingly right as Google rose from a valuation around $80bn to around $1.6 trillion.
The chart is compatible with this interpretation. It doesn’t say where the shares might be going but it could not be stronger.
Now let us look at some of those spring loaded shares that I was talking about.
Microsoft Accelerating AI Transformation Across Industries
Microsoft is a perfect example. We are now into the fifth six-month candlestick of consolidation which is plenty to support a strong rise one a chart breakout. We also have a strong pointer to which direction the shares are going to break (up) because they have given a double whammy buy signal using Coppock and my moving averages.
Just as an aside Coppock is working very well and giving fabulous signals for the stock market as a whole and for individual shares. What we are now testing is my second proposition that as well as giving great buy signals, shares very rarely decline against the background of a rising Coppock. If that works, and I think it may, that is going to be a game changer, especially for a leveraged investor like me.
I have found a blog on the Microsoft web site very similar to the one I have quoted from the Nvidia web site. Here I am just going to give you the heading.
The era of AI: How the Microsoft Cloud is accelerating AI transformation across industries
Judson Althorp, chief commercial officer, Microsoft, 24 April 2023
Like Nvidia, Microsoft is a great AI play and has the muscle and the global footprint to make a massive impact in this brave new world of generative AI. It also has the brand. Who hasn’t heard of Microsoft whereas Nvidia is a relative newly outside gamers and professional coders.
The argument for the shares, apart from the exciting fundamentals, is that we have a springboard consolidation and we know it is going to break higher because of the double whammy buy signal. Hubris maybe but it almost seems like a licence to print money.
My sensible daughter has asked me to temper that comment, bearing in mind such powerful forces as Murphy’s Law (whatever can go wrong, will) and the danger of Black Swans (unexpected developments like Covid and the war in Ukraine that come out of nowhere) so we always need to be more cautious than I typically am. As I have said many times I love to gamble. If I am not betting the ranch I get bored but I am also alert to danger. I also think that if you don’t have a go you can expect much to happen. Strike while the iron is hot and all that stuff.
I am also pumped because I am listening to some fabulous gospel music. I am not sure about God but I love His music.
This has the makings of a spectacular chart breakout, which is surely going to come sooner or later. Meanwhile we have a clear double whammy buy signal and we have a company which is classic 3G with tons of magic and a leader, Jeff Green, in the classic charismatic mould of Elon Musk and Jensen Huang.
I would say the breakout level here is the round number resistance of $100, which is the ceiling against which the price is banging. One day those sellers will be overpowered and the shares will be up and away. Very often this happens at a time when many other shares are doing well so it is easy to miss. We will keep an eye out on Quentinvest.
The Trade Desk Generates Soaring Cash Flow
In addition to strong top line performance, in Q1, we generated $109m in adjusted EBITDA or 28pc of revenue, exceeding our own expectations. As it typically occurs, when we outperform on the top line, it generally flows through to our bottom-line results as it did in this quarter. This outperformance led to record free cash flow of $177m in Q1, marking nearly $500m in free cash flow on a trailing 12-month basis, which is nearly 10 times the free cash flow we generated just three years ago. In the current environment, our consistent ability to grow our top-line revenue while generating meaningfully positive adjusted EBITDA and cash flow puts us in a strong position to continue investing for growth and grabbing land while others are forced to pull back.
Jeff Green, CEO, The Trade Desk, Q1 2023, 10 May 2023
Subscribers will recall that I have spoken in the past about the battle for territory. In the past many fast-growing companies prioritised this over profits and even cash generation but everyone is more cautious now, still battling for territory but making sure they can pay their bills and generate profits.
If we scroll down the charts we can see that they are all strong but the top one, Nvidia, is the strongest followed by Microsoft, then The Trade Desk and afterwards Hubspot. My theory is that Nvidia is leading where the others will follow.
Hubspot is very much an early bird generative AI play.
Hubspot an Early Bird Generative AI Play
Now, I want to double-click on innovation and share how we’re thinking about generative AI and why we are well-positioned to add even more value for our customers. We are in the early stages of a transformative shift. Generative AI is rapidly changing the landscape in three fundamental ways. It helps businesses generate content, generate insights, and generate code, all using natural language.
This will be a massive opportunity for SMBs and scaling companies. Activities that once took them time, money and deep expertise no longer do with Gen AI. And this shift will enable SMBs to reach more customers, serve them at record speed with unprecedented relevance. So, what does this really mean for marketing sales and service professionals? In the simplest terms, we believe AI will guide go-to-market teams and make them more effective.
This will fuel a new era of AI-guided growth for our customers. When we bring together the power of foundational models with deep contextual data in HubSpot CRM, we can help go-to-market teams drive better results. Marketers can use Gen AI to guide them in creating more effective block poles, email campaigns, and social content. Salespeople can use it to guide them to write better prospecting email and deliver more relevant insights for customers.
and service professionals can use Gen AI to anticipate customer needs, suggest resolutions, and offer proactive support. We believe AI won’t replace go-to-market teams. It will guide them to drive better outcomes. While there will be efficiency benefits, we’re even more excited about effectiveness gains and the ability to drive guided growth for customers.
While we are in the early stages of Gen AI, HubSpot has unique differentiators. First, we have unique data and broad distribution. HubSpot CRM data is unified and cohesive making it easier for AI to ingest and drive relevance. Second, we’re at the center of our customers’ workflows.
HubSpot is where work gets done. So, we can bring relevance to generate content and insights across the entire front office. We’re not another AI point solution. We are an all-in-one CRM platform powered by AI.
Third, we’ve always had a human-centric approach in companies with a human feedback loop or at an advantage with AI. We made a ton of progress in Q1 with the launches of content assistant in public beta and ChatSpot in public alpha. Since our launch in March, we’ve had over 40,000 users sign up for ChatSpot, and the early feedback has been very positive. Content Assistant has thousands of users to date, and we are seeing customers leveraging it daily for creating marketing emails, blog posts, landing pages, and more.
We’re ambitiously integrating AI across our entire CRM platform. So, our customers don’t have to become AI expert to reap the transformational benefits. I’m incredibly excited at the opportunity AI is creating to deliver even more value for our customers.
Yemeni Rangan, CEO, Hubspot, Q1 2023, 3 May 2023
Strategy – Back Buy Signals, Add on Breakouts
The Nasdaq 100 index is itself an example of the spring loading plus buy signal phenomenon. It has given a powerful double whammy buy signal, Coppock turned positive in March and is building a promising consolidation, from which there are good odds that it will eventually break out higher. This is an exciting prospect for investors.
There may be some resistance around the all-time peak at 16,500 plus or there may not. A characteristic of bull markets is that resistance levels do not put up much resistance.
Subscribers Not Receiving Their Alerts
In recent weeks we have heard from many subscribers, who were not receiving their regular alerts. Our web designer, and his wife (how’s that for dedication), have been putting in many hours to find out why. It turned out to be something mysterious (to me) about the inner workings of Mail Chimp, which I am glad to say have been sorted out. You should all be receiving your alerts in a timely fashion, Please let us know if this is not happening.
Analysts and investors often find it difficult to deal with a market that changes direction with great rapidity such as we are seeing in 2023. There is a natural tendency, especially for people who are trying to crunch the numbers, to think that an sharply rising stock market has risen too far, too fast and must fall back.
I do not pretend to have the answers on this but because I am not a number cruncher and typically, when I recommend a stock, have no idea what the price earnings ratio is or other key ratios for that matter, I can pay more attention to the chart and the bigger picture.
If the chart looks good and the big picture is exciting that is what makes a share a buy for me. At the moment the charts look good and generative AI is inspiring many exciting stories, which makes me feel positive.
