$PENG reports this Tuesday. The stock rallied nearly 200% last 3M and the Health Score Develops towards a stable area according to Stoxcraft. What will happen after the earnings? Back to ATH? Will Stoxcraft Upgrade the Health Score to a stable area? Will analysts set a new price target?
Hyperscalers are on track to spend $700 billion on AI infrastructure in 2026. The money is real. The assumption that every stock in this category benefits equally is not.
We scored five names across health, performance, and risk. The group splits cleanly into four profiles that each require a different mindset before you put capital in.
NVIDIA is the quality compounder
53% net profit margin. 61.6% return on assets. Altman Z-Score of 69.5 which signals zero financial distress risk. Q1 fiscal 2027 revenue hit $81.6 billion, up 85% year over year. Data center revenue alone was $75.2 billion.
NVDA has sold off sharply multiple times in the past two years. Its Health Score barely moved during any of them. That is the whole point. The underlying business does not become weak because sentiment shifts for six weeks. Investors who understand that distinction behave very differently during drawdowns.
Applied Materials is the picks-and-shovels play
Health Score 8.9. Inside the Stoxcraft Top 25 overall. AMAT does not build AI chips. It builds the machines that make AI chips possible. Record Q2 2026 revenue of $7.91 billion, up 11% year over year. Fourth consecutive earnings beat. Management raised its 2026 semiconductor equipment growth forecast to more than 30%.
The structural difference from pure-play chip stocks is that AMAT sells capital equipment across advanced logic, DRAM, and advanced packaging simultaneously. When one segment softens, the others provide a buffer. That diversification keeps the Health Score elevated through cycle fluctuations.
Nebius Group is the turnaround candidate
Revenue grew over 770% across three years. Three-year CAGR above 106%. Current ratio of 9.6 with total debt of only around $50 million.
The risk is right there in the same data. Net loss of $394 million. The company is targeting $7 to $9 billion in annualized revenue by end of 2026, implying growth of 500% to 900% from its recent starting point.
A turnaround candidate is not a broken stock. It is a stock where the health trajectory matters more than the current health level. NBIS has the liquidity to fund its growth phase without near-term balance sheet stress. The question is whether revenue growth converts into narrowing losses. Tracking the Health Score each quarter is the most direct way to monitor whether the story is actually working.
Astera Labs and AST SpaceMobile are where the Risk Score hits the ceiling
stoxcraft.com/stocks/alab stoxcraft.com/stocks/asts
Both carry Risk Scores at or near the maximum. The reasons are completely different.
ALAB has a Health Score of 7.2 and real fundamentals. Q1 2026 revenue up 93% year over year. The company is profitable. The Risk Score of 10.0 is not about the business model. It is about the stock price. ALAB fell roughly 60% from its late-2025 peak. At a forward PE above 260x, any execution miss creates instant and severe drawdowns. Analyst consensus is Buy with an average 12-month target of $244.97. The long-term story is real. The path will not be smooth.
ASTS is a different category of risk entirely. Building a space-based cellular broadband network that connects directly to ordinary smartphones. Net loss exceeding $487 million on trailing twelve-month revenue of roughly $85 million. Guided for $150 to $200 million in 2026 revenue with $3.5 billion in cash and over $1.2 billion in contracted partner commitments. Genuine commercial progress from near zero. But solvency is not the same as commercial proof at scale. A Risk Score of 9.9 is the system's honest description of that position.
🔍 Full breakdown: stoxcraft.com/blog/ai-infrastructure-stocks-what-four-score-profiles-reveal-about-the-2026-category
Same category label. Four different financial realities. Before you buy the AI infrastructure trade, which of these four profiles are you actually comfortable holding through a drawdown?
Drop your take below.
Die drei oben genannten Aktien waren in den letzten 12 Monaten sehr erfolgreich, aber welche ist in Ihrem Fall die beste? In welcher sehen Sie das größte Potenzial?
The three stocks above have been very successful over the last 12 months but which one is the best in your case? In which one do you see the greatest potential?
504 stocks. One fund. And 10 names controlling nearly 38% of all of it.
What VOO actually is
VOO tracks the S&P 500 at 0.03% expense ratio. Cheaper than SPY at 0.09%. Way cheaper than QQQ at 0.18%. On paper it looks like the safest, broadest bet you can make on the US market.
Except it is not quite that simple.
The part most explainers skip
NVIDIA alone sits at 7.6% of the entire fund. The top 10 holdings combined control nearly 38%. Tech stocks make up over a third of the S&P 500 right now, which is unprecedented in the modern era of index investing.
You are not buying 500 companies equally. You are buying its 10 biggest tech stocks with 494 others quietly along for the ride.
The inflow numbers are historic
VOO has pulled in $60B in net inflows in 2026 alone and is closing in on $1 trillion AUM. Everyone is buying it. Not everyone understands what they are actually buying.
🔍 Full breakdown: stoxcraft.com/blog/voo-etf-explained
So genuine question: did you buy VOO knowing about the concentration, or did the "500 companies" pitch make it feel more diversified than it is?
Drop your stock and i will let you know on account of the companies Health Score if the stock is fundamentally in good conditions and if yesterday´s drop can mean much more as just a tech correction.
Today was the day I had to stop ignoring MU and here are my 3 reasons for it:
1. The Most Bullish Price Target on Wall Street
UBS just raised its price target on $MU from $535 to $1,625, making it the single most bullish target on all of Wall Street, while maintaining its Buy rating. That implies a potential double from current levels. Analyst Timothy Arcuri projects EPS exceeding $100 through at least 2029, arguing that AI has permanently reshaped the memory market and justifies a full multiple rerating.
2. The Fundamentals Are Simply Outstanding
Q1 FY2026 revenue came in at $13.64B, up 57% year over year. Non-GAAP EPS of $4.78 crushed the $3.94 consensus. Q2 guidance calls for $18.7B in revenue. The Cloud Memory Business Unit nearly doubled to $5.28B at 66% gross margins. And on top of all that, Micron's Manassas facility just started producing the most advanced DRAM ever made in the United States.
3. A Health Score of 8.7 out of 10
According to Stoxcraft, $MU scores 8.7 out of 10.0 on their Health Score. That is exactly the kind of financial backbone I want to see before committing real money to a position. Strong fundamentals, strong execution, strong balance sheet.
Let´s move on to 1,000 USD!
I've been diving deep into photonics for a while now and the more I learn, the more convinced I am! This is one of the most underrated areas in all of tech infrastructure right now.
So what's actually going on? AI data centers are hitting physical limits. Copper cables simply can't move data fast enough anymore to keep up with what modern AI clusters demand. The answer? Light instead of electrons, Silicon Photonics.
NVIDIA figured this out a while ago. In late 2025 they shipped the world's first commercial co-packaged optics switches together with TSMC, Coherent, Lumentum and Corning.
Then there's the energy angle, which doesn't get talked about enough. Data centers are on track to consume around 1,050 TWh in 2026, roughly the entire power consumption of Japan. Some regions are already blocking new data center permits. Photonics isn't a nice-to-have here. It's a necessity.
The full value chain on my watchlist:
🔹 Layer 1 – Materials & Wafers: $GLW $AXTI $IQE $AIXA $AMS
🔹 Layer 2 – Core Photonic Devices: $IPGP $COHR $LITE $LASR $SIVE
🔹 Layer 3 – Components & Modules: $AAOI $MTSI $FN $VIAV $LPTH
🔹 Layer 4 – Systems & Equipment: $ASML $BESI $ASM $LPKF $MKS
🔹 Layer 5 – Test, Metrology & Yield: $CAMT $FORM $AEHR $ONTO $VIAV
Samsung is targeting full photonics integration into AI chips by 2028. And here's the thing, most investors are still fixated on the usual AI names while the actual infrastructure powering all of it flies completely under the radar.
That's exactly where I see the opportunity. Am I missing any "hot! photonic players?
$TE surged over 23% to $7 this week after news broke that the $13.7 billion hedge fund Situational Awareness LP snapped up 10 million shares. The man behind it is Leopold Aschenbrenner, former OpenAI researcher, who built a $43.9 million position in T1 Energy alongside other companies tied to the AI infrastructure buildout.
Why am I in this? Honestly, the energy story behind AI is what gets me. Everyone talks about chips and models, but nobody builds a data center without solving the power problem first. T1 is positioning itself as a homegrown American solar and storage company with a direct focus on supplying hyperscalers. That is the kind of picks and shovels play I look for when a megatrend is still in its early innings.
Full transparency though, this one is not for the faint of heart. Stoxcraft rates T1 with a Health Score of just 0.7/10 and a Risk Score of 9.5/10, which puts it firmly in high risk territory. The company is still burning cash, with a net loss of $21.4 million in Q1, up 25% year over year. I went in with a small, capped position that I am comfortable losing entirely if things go south. That discipline matters when you are playing in this space.
Smart money is paying attention. So am I. But eyes wide open.
Every investor dreams of finding companies that multiply by 5, by 10, or by 100. It is the philosopher’s stone of investing, the holy grail, the elixir of life for people obsessed with looking at charts and reading fundamentals.
When I started investing, one of the books that fascinated me the most was 100 Baggers: Stocks That Return 100-to-1 and How to Find Them, by Chris Mayer. It was incredible. The promise was that instead of finding stocks that would make me rich at 67, they could let me retire at 35.
Since then, I have read other “studies” on the topic with the same enthusiasm. Unfortunately, they all have one fatal flaw: anecdotes, qualitative analysis, and little evidence of causality. My engineer soul was missing something more rigorous.
Luckily, I recently came across a paper that tries to go one step further: The Alchemy of Multibagger Stocks, by Anna Yartseva.
Although the paper is not perfect, far from it, it brings a more methodological and scientific approach to the subject. It does several things I like.
It starts with a review of what has traditionally been said about multibaggers, which is perfect for anyone who has never read anything on the topic. Then it tries to study what characteristics these companies shared, starting from the Fama-French five-factor model, and later adapting the model to multibaggers. In the process, it uncovers a few things that had rarely been discussed before.
Today’s post is about this paper and some of its most interesting conclusions. I have published the full article on my website, with a more detailed analysis, interactive widgets, and a more rigorous critique for anyone who wants to read it. In this article, I am only going to comment briefly on some interesting conclusions.
In the original post, I also go through the “anatomy of a classic multibagger”, which summarizes what was commonly known about multibaggers and is also very useful for anyone interested in the topic.
The study analyzes companies listed on the NYSE and NASDAQ, including ADRs, between 2009 and 2024.
The window starts just after the financial crisis and covers 15 very eventful years: bull and bear markets, COVID, inflation, interest rates, the banking crisis, wars, and commodity shocks.
It identifies more than 500 stocks that reached a 10x return, but only keeps those that maintained that level until the end and removes those with incomplete data. The final sample consists of 464 multibaggers.
What is interesting is that it does not only look at the 2009-2024 increase, but also at the companies’ prior history from the year 2000 onward. The idea is not simply to celebrate winners after the fact, but to look for signals that were already present before the big move.
The analysis starts with the Fama-French five-factor model, one of the most widely used frameworks to explain why some stocks earn higher returns than others.
The idea, simplifying a lot, is that a stock’s return can be explained by its exposure to several factors: market, size, valuation, profitability, and investment.
In other words, the model tries to explain how much a stock has earned by comparing it with what a risk-free asset would have earned and by seeing how much of that return comes from different known factors.
The appeal of the model is that it lets you ask a very useful question: did multibaggers earn so much simply because they were exposed to known factors such as size, value, or profitability, or was there something else?
And that “something else” is exactly what the study tries to find.
In a factor regression, beta measures how much a stock moves relative to the market. A beta of 1 means it moves more or less like the market; above 1, it is more sensitive; below 1, less so.
Alpha is what remains after explaining the return using the model’s factors: market, size, value, profitability, and investment. Put simply, it is the part of the return that the model cannot explain.
But be careful: alpha is not an explanation. It is a clue. It may reflect a real company advantage, a missing factor in the model, or simple statistical noise. That is why it should be treated as an interesting signal, not definitive proof.
The study uses the Fama-French five-factor model to see whether it can explain the historical returns of multibaggers.
The basic idea of the model is that, over the long term, small, cheap, profitable companies with prudent investment tend to do better. To test whether this also holds here, the study sorts the companies in the sample, between 2000 and 2024, into different groups:
When all of these are crossed, the result is 36 different portfolios.
The objective is twofold:
And that is where things start to get interesting: looking for more specific variables to understand where that extraordinary return really came from.
The table groups the companies by size, valuation, profitability, and investment, and colors the return of each combination to quickly show what works best.
The best portfolio appears among small, cheap, profitable companies with aggressive investment. In other words: small caps, with high book-to-market, good operating profitability, and strong asset growth.
The main conclusions are quite clear:
And the big surprise is investment. According to Fama and French, companies that invest aggressively should do worse. But here, almost the opposite happens: companies with higher asset growth achieve better returns. It makes sense. A company that wants to multiply cannot stand still. It needs to reinvest, grow, and build something much bigger.
Then, the study runs a regression to see how much the five factors explain. And here is the important part: operating profitability contributes little, these stocks have high beta, and alpha remains too high.
Translation: the five-factor model does not explain multibaggers very well. It captures part of the story, but it misses something important. And that is exactly where the interesting part begins.
Because the classic Fama-French model leaves too much alpha unexplained, the study tries to adapt it better to the case of multibaggers.
To do this, it tests different metrics for size, valuation, profitability, and investment: market capitalization, enterprise value, sales, book-to-market, P/E, price-to-sales, margins, ROE, return on capital, asset growth, EBITDA, and free cash flow.
In an intermediate version, the study changes some variables: it uses TEV for size, P/E for valuation, and EBITDA margin for profitability. But P/E ends up losing weight because it adds too much noise: it does not work for loss-making companies and explodes when earnings are very low. That is why the most useful valuation metrics end up being B/M and FCF/P, meaning how much free cash flow the company generates relative to the price paid.
The most interesting part is investment.
The study introduces a variable that detects when assets grow faster than EBITDA. And the result is strong: when a company expands assets faster than EBITDA growth, the following year’s return falls by around 22.8 percentage points.
The interpretation is quite clear: multibaggers need to invest, grow, and expand capacity. But that investment has to be accompanied by real EBITDA growth. If assets grow and EBITDA does not follow, the company is probably buying bad growth, inflating its balance sheet, or reinvesting at mediocre returns.
In short: the best multibaggers are not only small, cheap, and profitable. They also know how to invest aggressively without destroying returns. It is not about growing for the sake of growing, but about growing with profits behind it.
Here the objective changes: the author is no longer trying to see whether multibaggers fit into Fama-French, but to build a more complete model to explain their future returns.
To do this, she tests more than 150 variables: growth, valuation, profitability, quality, debt, solvency, momentum, interest rates, analysts, investment, R&D, marketing, and sector comparisons. Much more than the classic “small, cheap, and profitable”.
To separate signal from noise, she uses Hendry’s general-to-specific methodology: you start with a huge model and gradually remove what does not add value until you are left with something cleaner and more robust. First, you throw everything into the pot. Then you remove ingredients until the thing finally tastes like something.
The interesting part of the analysis is here: it moves from describing what multibaggers looked like after the fact to trying to identify which variables best explained their returns before they happened. It is not perfect, but this is where the most useful conclusions for investors appear.
The model works reasonably well: almost all coefficients have the expected sign. The market matters, size penalizes returns, valuation matters a lot, and investment only works if it is accompanied by real EBITDA growth.
The most important conclusions are:
There are also variables that surprisingly add little: debt, debt coverage, Altman Z-score, buybacks, dividends, share issuance, and R&D. But be careful not to misinterpret this: because the analysis only studies companies that survived and ended up being winners, there is selection bias. The fact that debt does not explain much within the survivors does not mean it does not matter when trying to avoid dying along the way.
In other words, the best multibaggers are not simply companies that grow a lot. They tend to be small, reasonably cheap, profitable companies that can invest without destroying capital and that are bought before the market has discounted too much future growth.
The study challenges some dogmas about multibaggers. Not because growth does not matter, but because isolated accounting growth explains less than expected. Valuation, free cash flow yield, size, interest rates, and investment quality matter more.
In short: a multibagger is not simply “a company that grows a lot”. According to this study, the most attractive combination would look more like this: a small, cheap, profitable company, with good free cash flow yield, capable of investing without destroying capital, and bought at a moment when the market is not yet too excited.
So yeah, it was never going to be easy.
---
I have left a lot out of this article, so here is the link to my original post, where I explain everything with much more detail and nuance.
The original post includes “the anatomy of a classic multibagger”, all sections explained in greater detail, and 3 additional appendices:
Link here: https://www.jeravalue.com/en/blog/10x-stocks-the-dna-of-multibaggers
(It is completely free without paywall)
Amazon has been one of the quietest stories in mega-cap tech this year and one of the strongest. The stock is up over 30% in the past month alone, driven by AWS momentum, advertising growth, and a string of AI infrastructure deals that are reshaping how the market thinks about this business.
Tonight the numbers have to show up.
Consensus expects revenue of roughly $177B, up ~13% year-over-year, with AWS expected to grow around 26%. The more aggressive call - UBS is projecting 38% AWS growth in 2026 - is what moves the stock if it proves closer to right. Advertising revenue is forecast around $16.8B, up 21%. The P/E has already compressed from ~50x in Q2 2024 down to ~30x by Q4 2025, so the valuation setup is meaningfully better than it was a year ago.
🔍 Full AMZN screener breakdown: stoxcraft.com/stocks/amzn
The revenue mix tells the real story of what Amazon has become. Online stores are still the largest segment but AWS and advertising are where the margin lives. Amazon committed $25B to Anthropic, signed agentic AI deals with Meta to run workloads on Graviton chips, and is guiding $200B in total capex for 2026. It is building infrastructure at a scale very few companies can match.
Q4 2025 was the one blemish in an otherwise clean earnings run - net income missed despite strong AWS and advertising. The market will be watching Q2 guidance closely, particularly operating income, which some analysts expect could come in below consensus even if revenue holds.
Analyst consensus is Strong Buy. Average price target sits at $283, with the street high at $360.
The business is as diversified and well-positioned as it has ever been. Tonight is about whether the numbers match the setup.
Rebound time for Oklo?
Here is a critical evaluation about the opportunities and risks the stock offers.
Opportunities
Data centers, especially those supporting artificial intelligence, require large amounts of reliable electricity. Nuclear energy provides stable, carbon-free power, which positions Oklo well if demand continues to grow.
Oklo focuses on compact nuclear reactors that are designed to be faster and cheaper to deploy than traditional plants. If SMRs gain widespread adoption, Oklo could benefit as an early entrant.
The company is part of a relatively small group developing advanced nuclear solutions. Strategic partnerships, particularly with large technology companies, could accelerate commercialization.
Oklo aims to reuse nuclear waste as fuel. If technically and economically viable, this could reduce costs and create a competitive advantage.
Many governments are reconsidering nuclear energy as part of climate strategies, which could lead to subsidies, regulatory support, or faster approvals.
⸻
Risks
Oklo is still a pre-revenue company. Its valuation depends on future expectations rather than proven financial performance.
Nuclear projects face strict approval processes. Delays or unfavorable decisions could significantly impact timelines and costs.
Designing, licensing, and building reactors is complex. Delays, cost overruns, or technical issues are common in the nuclear sector.
The company will likely need substantial capital to scale. This could lead to issuing new shares and diluting existing investors.
Oklo competes not only with other nuclear developers but also with rapidly advancing renewable energy technologies combined with storage.
The stock is influenced heavily by expectations around AI and clean energy. This can result in large price swings without corresponding fundamental changes.
The risks can be also seen on Stoxcraft‘s Health Score Rating. With just 1.9 of 10 points the rating confirms the fact that the Company doesn‘t earn any money yet.
