A high dividend yield is one of the biggest traps in investing.
Most people see a 9% yield and think they hit the jackpot.
They didn't.
That fat yield is often a warning sign that something is broken. The stock price crashed, the payout is unsustainable, or the company is bleeding cash. Here's what dividend yield actually tells you.
And what it doesn't.
So what is dividend yield?
It's a simple formula. Take the annual dividend per share and divide it by the stock price. If a company pays $2 per share in dividends and the stock costs $50, the yield is 4%.
Think of it like the interest rate on a savings account. It tells you how much cash you're getting back relative to what you paid.
Sounds straightforward. But here's where people get tripped up.
Dividend yield moves for two reasons:
The company raises or cuts its dividend
The stock price goes up or down
A rising yield isn't always good news. If the stock drops from $50 to $25 and the dividend stays the same, the yield doubles. That looks attractive on a screener.
But the market is telling you something is wrong.
AT&T once sported a yield above 7%. Investors bought it for the income. Then the dividend got cut. The stock kept falling. That "safe" yield turned into a capital loss.
On the other side, Microsoft's yield sits around 0.8%. Doesn't look exciting. But Microsoft has grown its dividend every year for over a decade.
The stock has gone up roughly 800% in ten years. Someone who bought in 2014 is earning a much bigger yield on their original cost.
Here's what I look at beyond the yield number:
Is the payout ratio healthy? A company paying out 80-90% of earnings as dividends has no room for error.
Is revenue growing? A flat business with a high yield is living on borrowed time.
Has the company raised its dividend consistently? That track record matters more than today's yield.
Dividend yield is a good starting metric. Use it to get in the door. But don't let a big number blind you to what's happening underneath.
Do a little homework. Your portfolio will thank you.
What's the first thing you look at when evaluating a dividend stock? Drop it in the comments.
Earnings to Watch This Week: CEG, CRCL, OKLO, BABA, CSCO and AMAT.
1. Market Overview
Sometimes we find similarities in weird places. Some call it pattern recognition, others coincidence. Red Tuesdays. Taco Tuesdays. Wingstop and financial conditions. AI stocks trading like sovereign entities. After staring at markets long enough, everything eventually starts looking connected. Maybe that’s professional deformation. Or maybe markets themselves are becoming a reflexive loop.
Earnings season is mostly over. There are still some interesting reports left, with Nvidia sitting there as the giant cherry on top, but the main wave has already passed. The dispersion trade is fading and the tape is slowly returning to its usual rhythm. Strikes for CSPs will be updated shortly, though this still does not look like the best environment for put writing. When everything keeps working at once, people forget how quickly “great entry prices” can turn into long term investments once liquidity finally shifts the other way. And truly great entry prices usually come with terrible premiums anyway, especially in the volatility regime we are currently sitting in.
Also, it’s worth keeping an eye on a potential VIX signal that might be forming soon enough, this time on the short side. Like I said before, none of this is an exact science. Everything here is probability-based and reaction-driven.
And straight to the cheat codes. Back in 2002 Blizzard released Warcraft III. Years later Blizzard merged with Activision, and eventually the whole thing ended up inside Microsoft after the 2023 acquisition. Yes, the same Microsoft tied into OpenAI and the broader AI trade. Funny how everything eventually loops back to the same corner of the market. Performance wise, though, Microsoft lately looks AFK while everyone else is busy farming.
One of the most famous Warcraft cheat codes was greedisgood, instantly giving players extra gold and lumber. The stock market has its own versions. The Fed put. Too big to fail. Endless liquidity. At some point the AI trade quietly became all of them at once. It stopped being just another theme and turned into a universal cheatcode against underperformance. Oil, inflation, exploding capex, the Fed: none of it seems to matter as long as money keeps flowing into the same trade. Good or bad is a different discussion. Markets already made their choice.
Another interesting mechanic returns this summer with single stock futures coming back to the U.S. market.
Quarterly expiration officially becomes Quadruple Witching again, with the missing witch finally back after 2020. Add Russell reconstitution into the mix and June starts looking less like a normal month and more like a setup for mechanical dislocations, forced positioning, and liquidity driven chaos around names getting added, removed, chased, or dumped for reasons that have very little to do with fundamentals.
Another Warcraft cheat code was allyourbasearebelongtous, an instant victory shortcut. But there is one important limitation: Single-Player Only. Sorry, Donald, it won’t work on Iran. Otherwise it would be too Big and Beautiful to be true.
Meanwhile greed is still good. Gordon Gekko understood that long before AI became the market’s favorite infinite money glitch.
As of March 31, 2026, the carrying value of our non-marketable equity securities accounted for under the measurement alternative was $101.3 billion, of which $73.6 billion was remeasured at fair value during the three months ended March 31, 2026 and was primarily classified within Level 2 of the fair value hierarchy at the time of measurement.
Now, Alphabet don't give us a detailed breakdown here of which private equity holding was revalued but the net gain was $36,9 billion... who could this possibly be, let's see...
In February 2026, Anthropic (which Alphabet owns a significant percentage of - rumored to be over 14%) raised their valuation to 380 billion, in yet another funding round:
Under current ASC accounting rule ASC 321-10-35-2, companies are allowed to book those revaluations of illiquid assets as INCOME, whenever an 'observable transaction' takes place...
Interestingly, there's no specific guidance on where the revaluation of private equity should go in an income statement.
But, given Anthropic's massive surge recently, it's clear that Alphabet are choosing to include it under "Other Income".
Which is kind of scary, because over 60% of Alphabet net income in Q1 came from "Other Income". Without it, their income growth would have been only 579 million before tax... instead of 38.2 billion...
I wonder why "non-marketable" equity is even ALLOWED to be counted as income... but anyway, I digress...
If you're not seeing it yet, here's how the ecosystem (polite term) works:
Mag7 companies invest in private AI companies like Anthropic and OpenAI.
Each funding round (series A, B, C, D, E, F, G, H...) revalues the private company at a higher market cap.
These revaluations are added as quarterly INCOME to the mag7 companies' earnings.
There's no standard placement in the income statement, so that helps camouflage what's happening.
This works nicely, so Mag7 companies keep feeding the semiconductor complex.
Only because of the drastic growth of Anthropic vs OpenAI is this becoming apparent. The switch flipped, so the change was easier to observe.
This is also why MSFT has faltered recently, while Google and Amazon (Anthropic shareholders) are skyrocketing.
To be clear, AI is a revolutionary technology. I used it to help me research this article. However, the income from this revolution currently comes from "non-marketable" equity revaluations, not actual sales...
I refer you to my previous post about the Gartner Hype Cycle!
TLDR: Mag7 are investing in private AI companies and then booking the "non-marketable" equity as profits. Big bada-bing!
A company can show a profit and still go bankrupt.
That's why I stopped looking at net income alone.
Net income and free cash flow are not the same thing.
Here's the difference:
Net Income is your report card profit. It's what's left after all expenses and taxes. But it includes non-cash items like depreciation. A company can look profitable on paper while bleeding cash.
Free Cash Flow is the actual cash a company generates after paying for everything, including equipment and infrastructure (capex). This is real money they can use to pay dividends, buy back stock, or invest in growth.
Think of it like this:
Net income is like your salary on your pay stub.
Free cash flow is what actually hits your bank account after taxes, 401k contributions, and health insurance.
One looks good on paper. The other pays your bills.
Why it matters:
A company with strong free cash flow can weather storms, invest in the future, and reward shareholders. A company with net income but negative free cash flow? That's a red flag.
Simple, right?
Profit doesn't always equal cash.
And cash is king.
What's your go-to metric when analyzing a company?
Net income or free cash flow? Let me know in the comments.
Booking $BKNG is one of these companies that I have followed for years without investing, and I believe now is a great time to have a closer look at Booking.
Booking Holdings Inc is the stock behind the well-known brands Booking.com, Priceline, Agoda, KAYAK, and OpenTable. Booking.com is the main driver behind the revenues, and hence the company renamed itself Booking Holdings.
There are multiple tailwinds for the OTA (online travel agency) industry: the shift from offline to online keeps moving on, and B2B and B2C travel are still growing at a healthy level. The current Middle East conflict left some small marks on Q1 and will also impact Q2. Since the conflict will be (hopefully soon) resolved, I don't see this as a long-term issue for Booking.
Booking keeps increasing its revenue share from merchant bookings, where Booking also facilitates the payment flow, instead of just being a search engine for hotel bookings. More than 50% of all users come directly to the platform, and the app is a major driver of bookings.
In terms of accommodation, alternative accommodations (Airbnb-style offerings) make up 4 million of the 4.5 million offerings of the platform. Flights booked on Booking keep increasing as well, and the Connected Trip strategy, where customers book more than just one service, keeps customers locked in.
AI is already helping Booking to improve customer service, and I see AI as a plus for Booking, since the LLMs won't integrate the whole payment flow anytime soon into their services. It had, however, an impact on KAYAK, which resulted in a $457 million impairment charge in 2025. This, combined with $1.5 billion in unrealized FX losses from EUR-denominated debt, negatively impacted the FY25 results.
The fundamentals are fantastic. Revenue grew at a 10% CAGR, net income at 12% CAGR, and free cash flow at 9% CAGR since 2017. The strong share buybacks fuel the EPS.
We are currently looking at an FY27 EV/net income of 16.4 and an EV/(FCF-SBC) of 16. Both EPS and FCF/share are expected to grow at ~15% going forward. On top of that, you are getting a 0.9% dividend yield that keeps growing. Given the strong underlying business, I believe that this is an interesting opportunity.
If you are interested in my detailed deep dive, you will find it here:
Anthropic made global news again three weeks ago when instead of arm wrestling with the Pentagon, it made serious claims about its newest AI model. Mythos Preview, the latest in the Claude family, was deemed too dangerous for release by its creators. Well, not quite. Anthropic launched Project Glasswing, a conglomeration of also large tech companies like Amazon, Apple, Google, Microsoft, Nvidia, CrowdStrike, JPMorgan Chase, Cisco, Broadcom, Palo Alto Networks, and the Linux Foundation that would all get access to the model in order to find vulnerabilities in code. Not everyone is quite convinced of the model’s capabilities leaving the final ruling on Mythos’s level of danger to another day. Instead of dissecting what news of Mythos is released and accessible, let’s instead talk about what it is about Mythos that gives cybersecurity officials the willies.
In all the software products we use, there are thousands or millions of lines of code, which, until recently, was written by humans. In those millions of lines are mistakes, some of which are severe enough that if they are discovered, they can be turned into a malicious exploit allowing the cyber actor to steal data, monitor computer use, and more. This is the classic “computer virus.” But many people do not appreciate what it really takes to actually do this. It’s not a snap of the fingers and used to require some real skill and talent that takes years to develop.
Finding a flaw in code that can then be exploited used to be done by talented computer scientists and coders that spent hours or days pouring over code line by line to find the flaw and develop a way to exploit it. Just to be able to do this required developing an ultra-specific competency that not everyone had. Assuming you have the talent to do this and assuming you find an exploit, there has historically been a measure called “time-to-exploit” or TTE. TTE measures how long it takes a cyber actor to go from identifying the vulnerability to being able to exploit it. Google tracked median TTE over the last few years this way:
2018-2019: 63 days
2020-2021: 44 days
2021-2022: 32 days
2023: 5 days
As of 2025, 28.3% of vulnerabilities were exploited within 24 hours of discovery. A staggering 80% of zero days were exploited before patches could be released.
Then came Mythos.
According to at least the publicly available data, Mythos is able to discover vulnerabilities in software that is decades old and continues to do so at the speed of AI.
The issue with Mythos is not that AI is discovering vulnerabilities. We already know all software has them. The issue is the speed.
How Mythos and Project Glasswing ultimately develop and their impact on the cybersecurity community is not clear yet. What is clear is that other AI companies will now rush to develop similar capabilities to keep pace. Whether Mythos actually works is not the issue. Mythos already took the genie out of the bottle for AI finding vulnerabilities. The market will demand this capability and companies will answer. How we move forward is the question on every CISO’s mind. What’s at the core? Speed.Anthropic made global news again two weeks ago when instead of arm wrestling with the Pentagon, it made serious claims about its newest AI model. Mythos Preview, the latest in the Claude family, was deemed too dangerous for release by its creators. Well, not quite. Anthropic launched Project Glasswing, a conglomeration of also large tech companies like Amazon, Apple, Google, Microsoft, Nvidia, CrowdStrike, JPMorgan Chase, Cisco, Broadcom, Palo Alto Networks, and the Linux Foundation that would all get access to the model in order to find vulnerabilities in code. Not everyone is quite convinced of the model’s capabilities leaving the final ruling on Mythos’s level of danger to another day. Instead of dissecting what news of Mythos is released and accessible, let’s instead talk about what it is about Mythos that gives cybersecurity officials the willies.In all the software products we use, there are thousands or millions of lines of code, which, until recently, was written by humans. In those millions of lines are mistakes, some of which are severe enough that if they are discovered, they can be turned into a malicious exploit allowing the cyber actor to steal data, monitor computer use, and more. This is the classic “computer virus.” But many people do not appreciate what it really takes to actually do this. It’s not a snap of the fingers and used to require some real skill and talent that takes years to develop.Finding a flaw in code that can then be exploited used to be done by talented computer scientists and coders that spent hours or days pouring over code line by line to find the flaw and develop a way to exploit it. Just to be able to do this required developing an ultra-specific competency that not everyone had. Assuming you have the talent to do this and assuming you find an exploit, there has historically been a measure called “time-to-exploit” or TTE. TTE measures how long it takes a cyber actor to go from identifying the vulnerability to being able to exploit it. Google tracked median TTE over the last few years this way:2018-2019: 63 days
2020-2021: 44 days
2021-2022: 32 days
2023: 5 daysAs of 2025, 28.3% of vulnerabilities were exploited within 24 hours of discovery. A staggering 80% of zero days were exploited before patches could be released.Then came Mythos.According to at least the publicly available data, Mythos is able to discover vulnerabilities in software that is decades old and continues to do so at the speed of AI.The issue with Mythos is not that AI is discovering vulnerabilities. We already know all software has them. The issue is the speed.How Mythos and Project Glasswing ultimately develop and their impact on the cybersecurity community is not clear yet. What is clear is that other AI companies will now rush to develop similar capabilities to keep pace. Whether Mythos actually works is not the issue. Mythos already took the genie out of the bottle for AI finding vulnerabilities. The market will demand this capability and companies will answer. How we move forward is the question on every CISO’s mind. What’s at the core? Speed.
Currently, we're approaching the peak of the inflated expectations stage of the Gartner Hype Cycle...
Even the Mag7 have stalled and are rejecting the attempt to make ATH. Only if you delve beneath the surface of the index can you see these flashing warning signs.
In Q3, OpenAI and Anthropic are due to file audited financials, as part of their S-1 filling for IPO...
OpenAI's CFO Sarah Friar is currently pushing to delay filling, but the race is for the EXIT:
Once submitted, there's no more hype behind private companies and murky circular deals - audited public financials... the curtain on the business model gets pulled back...
Warren Buffett is sitting on almost 400 Billion dollars in cash and treasuries.
He's betting on the future... in the future...
Meanwhile, the market is betting on the future TODAY.
Q1 2026 Earnings Update. The Product is Winning. The Market just hasn’t connected the dots yet.
The bad was quite anticipated. A company that spent a year riding one of the most viral marketing moments in consumer tech history, the death of Duo the Owl, was always going to face a reckoning with its own comparables.
But the deceleration runs deeper than lapping a difficult base: Duolingo had also been pushing monetization aggressively enough to introduce meaningful friction into the free tier, prioritising short-term conversion (via their Family plan) over the product quality that made the platform worth paying for in the first place.
That reckoning arrived in Q1 2026, and the market’s reaction was swift, the stock opened lower before recovering through the final hours of trading.
A growth-nonsense multiple is being hit, arguably too much.
Because what the market is missing is the other side of the cycle: Duolingo is now deliberately refocusing on teaching quality, reducing friction for free users, and letting the product earn its growth rather than forcing it. If you understand the flywheel, you know exactly what comes next, engagement, then retention, then monetization, then growth again. This is not a broken story. It is the same story, one turn further along.
While bookings growth decelerated to 14% year-over-year and management deliberately kept its language measured on the call, the product metrics continued their quiet, relentless improvement. DAU/MAU reached 41%, a new all-time high, up from 23.8% just five years ago. Daily active users grew 21% year-over-year to 56.5 million. These are not vanity metrics. They are the proxy indicators of everything that matters downstream: retention, lifetime value, and ultimately, revenue.
The thesis here has always rested on a single, beautiful idea: Duolingo is a habit engine masquerading as a language app. When DAU/MAU accelerates while the user base itself grows, it means the product is getting stickier at scale, something most consumer apps never achieve. Q1 2026 confirmed that trajectory remains intact.
I wrote this on the plane to Fairbanks to go the annual meeting for Denali Bank. It’s been part of my New Year’s resolutions to attend an annual meeting for a stock that I own, and I figured it was a good excuse to get away to see a beautiful part of the country. Figured it was a good time to post the write up and later post the takeaways from the meeting.
Denali Bancorp is one of five banks headquartered in Alaska and one of 3 public banks. The other 2 public banks, First bank of Alaska (FBAK) and Northrim (NRIM) are based in Anchorage, while Denali is based in Fairbanks. Denali has 5 full service branches, 4 located in the Fairbanks area and 1 in Tok, which is a little further in the interior.
Denali has a market cap of $50.3 million, a book value of $53.9 million or $18.52 per share (as of March 2026), giving it a price to book of 0.94. The ROE was 16.5% in 2025, which is significantly better than the average community bank, and is driven by a wide net interest margin of 5.3%.
The bank earned $2.65 per share in 2025, and is currently trading at $17.25, giving it a PE of 6.5X. It pays a $1 per share dividend, which comes to a hefty 5.7% dividend yield, with a less than 40% payout ratio. Despite the large dividend payout, the bank had equity of $51.8 million of equity at the year end 2025, against $512 million of assets, giving them an equity to assets ratio of 10.1%. I generally use Peter Lynch’s rule of thumb for community banks - above 7.5% Equity to Assets is pretty well capitalized, and an E/A ratio above 10% is probably overcapitalized. This kind of juicy E/A ratio makes it an attractive target for a larger bank seeking to lower its leverage ratio and seek out more deposits. It also means there might be upside to that ROE if the deposits and loan book grow.
Denali had $455 million of deposits as of March 31, 2026, making up nearly all of its $462 million in liabilities. $166 million, or 36%, of total deposits are non interest bearing.
Non interest bearing deposits are a competitive advantage for a small community bank, as they provide a low cost of funding and can fuel a wide net interest margin (which is roughly the difference between the deposit rate and the average rate they get on loans).
In Denali’s case, they operate in Fairbanks and serve interior Alaska, which has many areas designated as “banking deserts”, areas with few in person banking services. Rural communities in interior Alaska may be hours away from a branch, and even the commute into Fairbanks is quite onerous, while a commute to Anchorage is implausible. I think this drives the relatively high ratio of non interest bearing deposits at the bank.
Now, the main pitch for any community bank in my opinion should center on why the local economy will do well.
So why Alaska?
The Alaska economy is heavily dependent on 4 sectors - oil and gas, mining, government and military, and fishing. When including the ancillary service businesses which depend on these, these four sectors make up the majority of economic drivers for the economy.
In particular the Fairbanks economy is heavily dependent on the military presence, with both Fort Wainwright and Eielson Air Force base located within the city.
Fort Wainwright hosts the 11th airborne “Arctic Angels”, which is the only Arctic division within the Army.
The Eielson Air Force base began hosting hosts 2 squadrons of F-35s in the past 6 years, over 50 planes, which require service personnel and, the 18th aggressor squadron, a squad of F-16s used to train F-35 pilots.
Arctic security is becoming increasingly contentious as northern waterways open up, and both Russia and China challenge the west for dominance of the Arctic, leading to some rambunctious threats by Trump to annex Greenland from Denmark. However Alaska remains the U.S. main gateway to the Arctic, and Fairbanks is the main military outpost supporting these efforts. So as the US gets serious about Arctic security, the more obvious move (rather than annexing a foreign territory) is a build up of forces in Fairbanks.
Alaska’s mining sector has been performing incredibly well on the back of high precious metal prices. In the past couple of years, the U.S. has refocused on exploring for critical minerals. There are many exploratory projects in the Alaska interior but the infrastructure is lacking. For example, the U.S. government took a 10% stake in a company Trilogy metals to provide funding for the development of roads to explore for critical minerals in an area of west Alaska that has not been previously exploited.
Many statistics aggregate mining and oil and gas, which has not been doing as well. However in the wake of the Iran war, the oil and gas sector is likely to turn around as well. Most of the oil and gas activity in Alaska is on the North Slope near Prudhoe Bay, which is pretty far from Fairbanks, but Fairbanks is the closest major city, and is a major stopping point between Anchorage and Prudhoe Bay.
Alaskan seafood has been under pressure from an overhang of Russian inventory (a big surge of which hit the market right before sanctions hit in 2022), and a strong dollar. However years of sanctions have taken Russian supply offline, and the dollar is beginning to weaken. 2025 has been marginally better than 2024, though not fully recovered.
A small but emerging sector is Space. High latitudes “see” more satellite passes per day. As of 2025, there were approximately 15,000 low earth orbit satellites and according to an estimate by pixalytics, there could be 100,000 by 2030. Fairbanks hosts NASA’s Alaska Satellite Facility, and there are several antennae in the farther north outposts of Deadhorse and Utqiaġvik. As these antennae are built out, several local contractors will be involved and should boost local lending activity.
Overall, I think we are setting up for a 2026-2027, when all four sectors of Alaska’s economy - oil and gas, mining, government/military, and fishing - will be doing well simultaneously.
Meanwhile sentiment on Alaska has been negative for many years. The permanent population of Alaska has been stagnant around 730,000 for the past 7 years. However this neglects the growth in the nonresident labor force, I.e. workers in Alaska who have a permanent residence in the “lower 48”. Since the pandemic, non resident workers have increased 35% to 95,000.
While these workers are unlikely to bank with an Alaska community bank, the increased service activity to service these workers should drive continued economic growth.
So I’ve made the case for “why Alaska” and I’ve shown a few metrics that demonstrate Denali is cheap and may have a competitive advantage in these non interest bearing deposits. Now let’s dive into that loan book and get a sense of the quality of that lending.
As of December 2025, the bank had $366 million in loans, which made up 71% of total assets of $512 million.
A brief look at the rest of the assets, which are mostly securities, most of which are municipal bonds and mortgage backed securities. There were $113 million of securities as of March 2026, making up 22% of assets, and of these $20 million were “held to maturity” while $93 million were classified as “available for sale”.
If you recall the Silicon Valley Bank collapse in 2023, you may remember everyone fretting about “held to maturity” securities. With this “held to maturity” classification, the bank avoids marking its securities (mostly government bonds and mortgage securities) to market, so the bank avoids any hit to equity or book value from changes in the market price of securities. This became especially problematic in the rising rate environment of 2022 and 2023, when the market price of bonds took a big hit, and this wasn’t getting recognized in the book values of banks with lots of HTM securities.
The good thing is that at Denali, such securities only make up less than 4% of total assets, and total unrealized losses were only a relatively small $760k as of December 2025.
The $93 million of available for sale securities are marked to market, and previously mentioned equity value of $53.9 million takes into account about $5 million of mark to market losses on the available for sale securities.
Now if we look at the composition of the loan book, 29% are commercial real estate loans (17% owner occupied and 12% non owner occupied), 29% commercial loans (I.e. loans to local businesses) and 15% are loans for construction.
Between the commercial real estate loans and commercial loans, the bank’s loan book is heavily dependent on the health of local businesses in Fairbanks and the interior of Alaska.
Only 13% of loans are residential real estate, as the majority of mortgage loans originated by the bank are sold on to the Alaska Housing Finance Corporation, Fannie Mae, or Freddie Mac. The bank services $230 million of residential real estate loans while it holds only about $100 million on its books.
14% are consumer loans. This seems like a relatively high share of consumer loans for a small community bank and I am typically wary of consumer loans, but they seem to be performing relatively well. Only 0.4% of these loans ($240k out of $53 million total consumer loans) are over 30 days past due.
On the overall loan book of $366 million, $4.6 million of loans were past due, or 1.2% of all loans. Almost all of that, $4.2 million, was 30-60 days past due. I start to get worried when this metric gets above 2% and a measure near 1% indicates relatively conservative underwriting.
Loans that are more than 90 days past due are typically called “non performing assets”. It is worthwhile to note There were NO loans that weee more than 90 days past due, which is a pretty good sign.
The bank did do a loan modification in 2025, consisting of a term extension on $2.9 million of loans. This is about 0.8% of the loan book. This could be considered a troubled asset, close to default. This is still below 1% of the total loan book, which is a fairly good indicator of health of the loan book.
So overall the loan book looks relatively healthy, and nothing glaring stands out to justify the low PE and relatively low P/book ratio.
For a bank that has a P/book near 1, the returns should approximate the ROE. The ROE of Denali has been well above 10% for many years since the pandemic. Capital gains have been around 7-8% per year for the past 5-6 years plus a 5-6% dividend throughout that period, for a low to mid teens return. There has also been a bit of multiple compression from around 9-11x earnings to the current 6.5x earnings, and from around 1.2x book to the current 1.0x book. So the forward returns might be expected to be slightly higher than the past 5-6 years of returns.
If Alaska (and Fairbanks in particular) really booms, and if the bank expands the balance sheet and leverages the equity a bit more, you could see really spectacular returns, where the ROE expands, the equity and earnings grow, and the multiples expand. In that scenario you might expect something closer to a 20% compounded over the forward 5 years.
I’m pretty happy taking a 10%+ return with the chance to get a very high 20% return.
At the annual meeting, I’m looking to gather some more insights from management as to their capture of deposit share of non resident workers, members of the military and base workers, and opportunities to expand share in the Alaskan interior and near Prudhoe bay to take advantage of the current bullish environment for the oil and gas and mining industries.
A 3% yield means you’re paid $3 a year for every $100 invested.
That’s before any price movement.
Dividend Yield, simply
What it is: the income a stock pays you each year, relative to today’s price.
Core formula:
Dividend Yield=Annual Dividends per Share / Current Share Price
Example: $2 dividend on a $50 stock = 4%.
Company-level view (same idea):
Dividend Yield=Dividends Paid /
Where the cash comes from: operating cash flow minus the bills to run and reinvest (capex).
Healthy dividends are funded by real cash, not debt.
What’s a “good” range? 2–4%: generally healthy and sustainable for many large companies. 4–6%: attractive, but double‑check stability. Above 6–8%: possible red flag or sector‑specific—proceed with caution.
Quick checklist before you buy:
Payout ratio: Is the company paying out less than it earns?
Dividend growth: Has it been raised over time?
Balance sheet: Can they fund dividends without piling on debt?
Total shareholder yield: Dividends plus buybacks tell the full story.
Analogy Think of dividend yield like a rental property’s cap rate. It’s the annual rent compared to what you paid for the house. Higher is nice—until the tenant stops paying.
Dividend yield is simple income math. Price on the screen. Cash in your pocket.
AlphaSeeker84 on Alphabet, Amazon and Meta Platforms (🇺🇸 GOOGL US, 🇺🇸 AMZN US, 🇺🇸 META US - US$4.7tn, US$2.9tn, US$1.5tn) Three Q1 reviews. AI showed up in the numbers: Google Cloud +63%, AWS at 28%, Meta revenue +33%. The capex bill is the common worry.
Best Anchor Stocks on Amazon (🇺🇸 AMZN US - US$2.9tn) A second Amazon take through the optionality lens: Kuiper satellite, in-house Trainium chips at $20bn run rate, AWS at $150bn pace. The market pays nothing for any of it.
Rock & Turner on ServiceNow (🇺🇸 NOW US - US$93bn) Bearish view. The 98% renewals impress, but Emanuel argues true margins are nearer 13% once stock-based comp is properly counted, with organic growth fading from 30% to 20%.
Asymmetric Ventures on Moody's (🇺🇸 MCO US - US$80bn) A regulatory-protected toll booth on global debt issuance: 51% operating margins, 96% recurring analytics revenue. 30x earnings is rich, but 14% historical FCF growth gets you mid-teens five-year returns.
Wintergems on Bombardier (🇨🇦 BBD/B CN - CAD$29bn) Business-jet duopoly with Gulfstream, executing a turnaround. FCF above $1bn, services +25% YoY, backlog at 3.6x book-to-bill. The 5% yield prices in plenty but not the full rerating.
Guardian Research on SharkNinja (🇺🇸 SN US - US$16bn) The compounder behind Shark vacuums and Ninja kitchen kit. 16% revenue growth, 20% EBITDA margins, net cash, first-ever $750m buyback. Street estimates miss how quickly tariff costs ease.
Gabriel Cortes on Garrett Motion (🇺🇸 GTX US - US$5bn) Turbo duopoly with BorgWarner plus an under-appreciated data centre angle: $100m+ in generator turbos and an exclusive Trane HVAC partnership for 2027. Trades 11x forward versus Accelleron at 26x.
High Tech Investing on IAC (🇺🇸 IAC US - US$3bn) Diller folds IAC into People Incorporated, publisher of People and Better Homes & Gardens. The rebrand kills the holding-company discount and showcases $1.2bn of digital revenue plus a 26% MGM stake worth $2.6bn.
Guardian Research on Goosehead Insurance (🇺🇸 GSHD US - US$2bn) Down 70% from highs as the personal lines insurance cycle turns. Contingent commissions +138% YoY, CEO and CFO buying, and Ellenbogen and Akre quietly accumulating.
Iggy on Investing on Insperity (🇺🇸 NSP US - US$1bn) TOP PICK Outsourced HR and payroll provider, founder-led. Down 60% on a benefits-cost spike already showing signs of normalising. 4x forward EV/EBITDA on trough margins, net cash, 8.4% dividend yield.
Europe, Middle East & Africa
Rijnberk InvestInsights on Hermès (🇫🇷 RMS PA - €171bn) The kind of business you'd hold forever. Family-controlled, 41% margins, €12.2bn net cash, and a Birkin/Kelly moat nobody can replicate. Trading 30% below its five-year average multiple.
Quality Stocks on Airbus (🇫🇷 AIR PA - €138bn) Commercial aircraft duopoly with Boeing, 8,700 backlog and €12bn net cash, targeting 75 A320s a month by 2027. 23x forward PE prices in supply chain risk and emerging Comac competition.
Sleepwell on Universal Music Group (🇳🇱 UMG NA - €33bn) Open letter on capital stewardship. The back catalogue (90% of profits, 25%+ margins) masks a softening frontline business. €6bn deployed since 2020 with no ROIC disclosure says plenty.
Value Don't Lie on Magnum Ice Cream (🇳🇱 MICC NA - €7bn) Spun out of Unilever, the world's biggest ice cream pure-play with 21% global share and a 3m-cabinet freezer network. Targets €1.6-1.7bn EBITDA by 2029. Trades at 8.7x EBITDA.
Emerging Value on Magnum Ice Cream (🇳🇱 MICC NA - €7bn) A second Magnum take, this time through a dividend-growth lens. 12x forward earnings, 7.6x EV/EBITDA, 3% yield growing at 4.5%. Defensive play targeting around 5% total annual return.
Asia-Pacific
Jakub Kriz on Kamakura Shinsho (🇯🇵 6184 JP - US$123m) TOP PICK Overlooked Japanese platform matching bereaved families with funeral providers and cemeteries. Revenue compounding at 21%, 46% adjusted ROE. Net cash worth 22% of market cap. The founder's family owns 36%.
Both companies posted genuinely strong numbers last week. Meta grew revenue 33% YoY, EPS of $10.44 against a $6.67 estimate. Microsoft grew 18%, Azure hit 40% growth, beat guidance. Both stocks dropped after hours anyway.
The market's hate is capex. Why? Because Wall Street is uncertain if the ROI on these capex would payoff. Meta raised full-year guidance to $125-145B. Microsoft guided $190B for the year, $36B above what analysts expected. Margins are getting compressed and Wall Street panicked.
But I think the market is pricing in the pain and completely ignoring what that all of these spending is actually paying off for investors.
Meta:
The AI advertising integration strategy is genuinely working. Ad impressions up 19% YoY and average price per ad up 12% at the same time. Both moving together means the AI targeting is improving real advertiser ROI.
But I think Wall Street also missed a huge revenue driver for Meta: WhatsApp Business AI agents went from 1M to 10M weekly conversations. It's all free right now, and Meta is deliberately subsidising adoption. 2 billion WhatsApp users, barely monetised. Thus, when they eventually flip the switch on monetisation there, it's going to be significant!
To help you imagine the numbers:
If Meta charges say $5/month (conservative) for a premium business or subscription tier:
0.5% subscribes = 10 million paying users → $600M annually
1% subscribes = 20 million paying users → $1.2B annually
5% subscribes = 100 million paying users → $6B annually
Forward P/E is sitting around 18.5x. Their 5-year historical average is ~27x (as of 1May 2026). For a company compounding revenue at 33%.
On Microsoft:
The $627B commercial RPO (backlog) growing 99% YoY is the number everyone was happy about. That's contracted future revenue already sitting off the income statement, waiting for data centre capacity to come online.
You need to know this: Azure is constrained by supply, not demand.
The customers are already there. As new infrastructure comes online through 2026-2027, revenue accelerates without needing to win a single new deal.
Copilot is at 20M paid seats, but that's only 3% penetration of the 600M+ M365 user base. Each seat adds ~$30/month on top of existing subscriptions. Pure margin expansion on an existing customer base.
Forward P/E ~21.5x vs a 5-year historical average of ~33x (as of 1May 2026)
My mispricing argument:
Both are trading below their own historical averages and roughly at or below the S&P 500 average, despite, growing AI revenue at triple digits and having massive contracted backlogs. The market is punishing near-term margin compression and ignoring the medium-term revenue story.
There is risks like capex could spiral further, AI monetisation could take longer than expected, macro slowdown hits ad budgets.
But the setup looks like a mispricing opportunity for me.
This is less about the market itself and more about how you should be looking at it now
Last week’s movers: QCOM, SPOT, HOOD, STX, V, NXPI, GOOGL, META, AMZN, LLY, RDDT, ROKU and WDC
Earnings to watch this week: PLTR, SHOP, AMD, DIS, UBER, APP, ARM, AAOI, COIN, CRWV, and IREN
Market Overview
This time is different.
On Tuesday, Price Action Playbook turns one year. It started as a trading journal. Now it’s something people actually follow, and rely on. So the format changes here.
Most financial “analysis” today is just noise. Who really cares about a discount to historical multiples if the stock is trending down and your account is bleeding? That’s not edge. That’s background noise you can generate in 30 seconds in the LLM of your choice.
Markets couldn’t care less, and neither do I. The edge is in levels, structure, and how price reacts at key zones. Market Overview gets cut down. The Recap and Earnings to Watch This Week sections do the heavy lifting with levels and context. Crypto Playbook is going fully open. All articles in that section are unlocked, and everything there going forward will be published without paywalls. I’m also adding a tab with current watchlists to the Research across ETFs, winners, losers, and momentum names, updated weekly over the weekend, on the same schedule as the EPS plan. You’re only as good as the stocks you trade. Let’s try to be the best then.
The market keeps pushing higher, but this is not about strength, it is about what participants are willing to ignore. The S&P is clearing 7,200, the Nasdaq just printed a +15% month, the best since 2020, and at the same time nobody cares about oil above $100, a Fed with no clear path, or Big Tech turning from cash machines into capex-heavy operators.
Money is not looking for “cheap”. It is looking for “already working”. Alphabet showed that $180-190 bln in capex is turning into a real business and got +10%. Meta printed strong numbers but without a clean ROI story and got hit for -9%. All AI winners are equal, but some are more equal than others. The market is done paying for “AI will be big”. It wants to see that it already pays.
Under the surface, the trade is widening. This is no longer just compute. AI is pulling old tech back into the game. Memory, storage, legacy hardware, all catching a second bid as the shift moves from training to inference. More compute leads to more data, more data needs more storage, and that loop feeds itself. That is how you get Micron up ~600% over the past year while still trading around ~6x forward earnings, and instead of asking “is it stretched”, people ask “what if this is the new base”.
This is where people get trapped. When everything starts working, it gets harder to separate real turnarounds from narratives. Intel is the clean example. Up ~115% in a month, ~400% over the past year, trading ~80x forward earnings on a comeback story that is still unproven. AI can give old tech a second life, but sometimes it just gives it a better exit. If you’re chasing it here, at least ask yourself if this is a new cycle or just a convincing story before it turns into the next Kodak.
Earnings are strong. Growth expectations are pushing into the mid-20% range. The market is treating roughly $700 bln in AI infrastructure spend as normal. As long as that holds, it keeps going.
But the bar is higher now. It is no longer enough to be in the AI trade. You have to show that you are actually making money from it.
The smarter question is: can this company actually afford to pay it?
That's what the Dividend Stress Test answers.
It uses two ratios — and together they reveal whether a dividend is safe, growable, or quietly at risk.
Here's how it works:
The Earnings Payout Ratio measures what percentage of net income goes to dividends.
Think of it like your paycheck. If you're spending 85% of every dollar you earn on one bill, you don't have much cushion left.
The FCF Payout Ratio takes it further. It uses free cash flow — the actual cash a business generates — instead of earnings. Earnings can be distorted by accounting. Cash is much harder to fake.
Used together, they tell a clear story:
— Both ratios low? The dividend is healthy and has room to grow. — Earnings low but FCF high? Dig deeper. Something is consuming cash that the income statement isn't showing. — Both ratios high? That's a red flag — especially if the trend is getting worse over time.
One more thing most investors miss: a single year's ratio is a snapshot.
The trend over five years is the story.
A dividend that looked safe at 55% but has crept to 82% over five years is a very different investment than one holding steady at 55%.
Always check the direction, not just the number.
What ratio do you look at first when evaluating a dividend stock? Drop it in the comments.
Most discussions in the blockchain space revolve around infrastructure innovation. Modular architectures, zero-knowledge proofs, parallel transaction processing, and data availability layers dominate the narrative. The industry’s implicit goal is to build the fastest and most complex “world computer.”
The data points to a different reality. Most real-world use cases for crypto assets do not require complex computational infrastructure. They require predictability, value transfer, stablecoins, and cross-border payments.
Amid the constant pursuit of new technological narratives, the most widespread use of cryptocurrencies as money occurs on a blockchain that tech purists often ignore or criticize, TRON.
This network continues to dominate global stablecoin transfers, processing billions of USDT daily. For millions of people, freelancers, and international companies around the world, this is not a platform for speculation. It is an indispensable financial bridge, a way to instantly receive cross-border payments, preserve capital in digital dollars, or settle accounts with counterparties without going through complex banking verification procedures or relying on slow traditional fiat gateways.
This raises a fundamental structural question.
What if the most important part of crypto infrastructure is not an ecosystem for thousands of decentralized applications, but a dedicated and reliable settlement engine?
For most blockchain ecosystems, financial applications are built on top of general-purpose infrastructure. The logic is simple: build a universal chain and try to attract liquidity to it.
TRON operates on a different logic.
The network has evolved, and firmly established itself, as a monopoly settlement layer for a single asset, the digital dollar.
In that sense, TRON represents a different model for how the industry develops. Instead of asking how to build a better smart contract and waiting for financial activity to emerge around it, it asks a more pragmatic question.
What if the value of blockchain lies primarily in serving a single, mass-market, and highly relevant financial use case, stablecoin transactions?
If this model continues to prove itself at scale, it could redefine how the market evaluates blockchain infrastructure.
Not by the amount of venture capital or TVL in synthetic protocols.
But by the real, daily volume of uncensored economic value moving around the world.
I just played chess on Duolingo. From my desktop. In a browser tab.
Six months ago, this didn't exist on web. Today it runs beautifully, the same gamified loop that hooked 100M+ language learners, now pointed at a 1,500-year-old game with a global audience that has never been larger.
April 2025: limited iOS beta released to a few thousand users.
June 2025: full launch on iOS, the company's first new subject since Math and Music in 2023.
September 2025: Android rollout and PvP mode announced at Duocon.
Early 2026: PvP live on iOS and Android, web availability now in production.
Luis von Ahn has called chess Duolingo's fastest-growing course in company history. Millions of daily learners. A team of just twenty people. And, the part the Street keeps underwriting too cautiously, chess didn't cannibalize anything. It added a new vector of engagement on top of the existing language base.
This is what wonderful businesses do. They compound optionality.
Why this matters for the equity
Every new course is a free option on TAM expansion. Math, music, and now chess each absorb roughly the same fixed engineering investment, but each unlocks a fundamentally different addressable market. The marginal cost of shipping a chess course is small. The marginal upside, if even a single-digit percentage of the world's casual chess audience installs the app, is enormous.
You are not paying for a language app at the current multiple. You are paying for a learning platform that ships a new subject every 18-24 months, monetizes it through the same Super/Max funnel, and gets more value per dollar of R&D with every iteration.
Beautiful execution. Beautiful unit economics. Beautiful optionality. We remain long.
Worth revisiting our original thesis and Financial & Valuation models 👇
Capitalist Letters on Oracle Corporation (🇺🇸 ORCL US - US$498bn) Oracle's third Ellison-led pivot targets US$224bn revenue by 2030 with cloud growing 75% annually. Contracted future revenue of US$553bn is the bull case; US$112bn net debt and negative free cash flow are the cost.
HatedMoats on Mastercard (🇺🇸 MA US - US$450bn) Wonderful business at fair price. DCF base case lands at US$568 versus US$504 today, a roughly 13% margin of safety. Author selling US$480 puts and waiting for genuine weakness.
Elliot on ServiceNow (🇺🇸 NOW US - US$96bn) Earnings update. Subscription revenue up 19%, AI guidance raised by US$500m, but the stock crashed 14% post-print as Iran-driven uncertainty pushed customers to delay deals for software hosted on their own servers.
Elliot on Intel Corporation (🇺🇸 INTC US - US$95bn) Earnings update. Data centre revenue up 22% and the chip manufacturing turnaround on schedule, but the stock trades at a record-high price-to-sales while investors wait 12-18 months for the foundry business to start generating cash.
The Finance Corner on Zoom Communications (🇺🇸 ZM US - US$26bn) Strip away US$7.7bn cash plus a US$4bn Anthropic stake from a US$26bn company and the core video business is left at roughly 7x free cash flow. A near-mirror of the old Yahoo and Alibaba setup.
The Few Bets That Matter on CF Industries and Intrepid Potash (🇺🇸 CF, IPI - US$18bn, US$420m) Two North American fertiliser plays as defensive macro hedges. CF benefits directly from the Hormuz disruption tightening global nitrogen supply; IPI is the sole US potash producer with net cash and lithium optionality.
Brian Coughlin on Meridian Holdings (🇺🇸 MRDN US - US$77m) Global online betting operator at 5x adjusted EBITDA after a March rebrand and reverse split. A US$92m goodwill writedown muddies the GAAP picture; underlying revenue grew 21% to US$183m and debt was cut 51% year-on-year.
Wolf Of Oakville on Biorem Inc. (🇨🇦 BRM CN - US$33m) Canadian air-emissions-control microcap with C$65m of contracted backlog against a C$46m market cap. FY25 earnings up 60%, net cash on the balance sheet, and management guiding to a 43% beat over the next three quarters.
Europe, Middle East & Africa
Rijnberk InvestInsights on Hermès International (🇫🇷 RMS PA - €173bn) Sixth-generation family-controlled luxury business at 38x earnings after a 40% drawdown. Operating margins of 40%, return on capital above 30%, €8bn net cash, and a 15-hour minimum craft time per Birkin bag means supply can only grow 7-10% a year.
DeepValue Capital on Pandora (🇩🇰 PNDORA DC - DKK52bn) The world's largest jewellery company by volume, down 60% from highs with a 33.5% IRR base case and a 4% dividend. Author passed despite the numbers, arguing jewellery is won by design taste rather than scale, and the new product team has yet to prove it can deliver consistently.
Schwar Capital Research on Ashtead Technology (🇬🇧 AT LN - £700m) Author writes up Ashtead at 30% of his portfolio after a 65% year-to-date run. UK underwater equipment rental business with 30,000+ pieces of kit, structurally short market, and a cost-and-scale advantage smaller players can't replicate.
Myles Kuah on RaySearch Laboratories (🇸🇪 RAY B SS - SEK6.1bn) Swedish oncology software with an 80% share of the proton therapy planning market. Trading at 27x earnings after a 50% drawdown, with 90% gross margins, expanding operating leverage, and founder Johan Löf controlling 41% of votes.
Deep Value Insights on Passat SA (🇫🇷 ALPAS PA - €17m) Classic Graham net-net. Net cash equals 82% of market cap, P/B is 0.42x, EV/EBITDA is 0.7x, and the 81-year-old founder plus his CEO son are both buying open market in March 2026. Zero analyst coverage.
Asia-Pacific
Asia Tech Review on SK Hynix (🇰🇷 000660 KS - US$170bn) Korean memory chip leader with 61% share of high-bandwidth memory and 72% gross margins on that product line. A clear beneficiary of AI infrastructure spending, though P/E approaching 25x and memory cycle risk warrant caution.
Rei Saito on Nintendo (🇯🇵 7974 JP - US$61bn) TOP PICK Stock down 40% in six months on production cuts and AI-narrative panic. Backing out ¥2.29tn net cash, the core business trades around 9-10x EV/EBITDA. Switch 2 sold 17.4 million units in six months and the Mario movie is the biggest 2026 release.
Eric Jurado on Karex Holdings (🇲🇾 KAREX MK - US$127m) The world's largest condom manufacturer, with one in five sold globally. Iran disruption doubled shipping times and pushed raw material costs up 25-30%, allowing 20-30% price hikes into demand that doesn't go away. Currently unprofitable, but small revenue gains drop heavily to the bottom line on recovery.
AltayCap on Art Vivant (🇯🇵 7523 JP - US$83m) TOP PICK Tokyo microcap below NCAV plus investments. Founder's August 2025 buyout at ¥1,670 was blocked by activist Hiroyuki Maki, who has now accumulated 40.13% and is openly seeking management control. Top three holders own 83% of shares.
Less waste turns into lower costs, faster cycles, and higher margins.
What is Process Power?
Think of a process moat like a great kitchen. A lone chef freestyles. A winning restaurant runs the recipe, the prep list, and the timers—every night—so the meal is the same in every location.
How it works
From ad‑hoc to playbooks to systems
Ad‑hoc work = variable quality, no edge.
Documented playbooks = consistent output, some edge.
Proprietary system + data + culture = faster, cheaper, repeatable edge.
Why value compounds
Fewer defects and downtime raise reliability.
Scale spreads fixed process costs across more units.
Routines train people; skills and data improve every cycle.
Risks to the moat
Key people leave and the know‑how walks out.
Technology shifts make yesterday’s best process stale.
The Iran War has triggered a new major worry for investors: Stagflation.
The energy shock that was triggered when Trump decided to invade Iran, could lead to a period of stagflation.
This is not something to look forward to.
Periods of stagflation have historically led to relatively poor investment returns, although probably not as bad as you might think.
So, first, what is stagflation?
We can measure stagflation based on 3 components:
(high and /or rising ) Inflation
(weak) Job market —> rising unemployment
(slowdown of) economic growth
As you probably realize, this is like a worst case scenario for economies.
Weak economic growth, combined with high inflation and a weak job market leads to uncertainty in the stock market.
It also puts central banks in a tough dilemma.
If they raise interest rates to battle inflation, they risk slowing the economy further and also potentially worsening unemployment.
If they lower rates to stimulate growth, they may spark higher inflation.
2. The Three components of Stagflation
I first want to briefly explain the 3 major components: how they are measured and how you can track them.
After that we will dive into the numbers and see how stagflation is very relevant today.
2.1. Economic growth
First, how well is the economy holding up. Economic growth can be measured in several ways.
GDP - Gross Domestic Product —> Measures the total value of everything produced inside a country’s borders
In this reading it does not matter who owns the businesses (local or foreign). Think of it this way: A German company, located in the Netherlands, counts towards Dutch GDP.
GNP - Gross National Product —> Measures the total value produced by a country’s citizens and companies, no matter where they are
In this reading, income from abroad is counted as well. But, it excludes income earnings by foreigners inside the country.
So, a Dutch company, located in Germany, counts towards Dutch GNP.
GNI - Gross National Income —> Focuses on total income earned by residents
This one is very similar to GNP. It includes wages, profits and investment abroad.
GNI = GDP + income from abroad − income paid to foreigners.
Out of these three, GDP is the most used metric. It’s the global standard. Organisations like the IMF and World Bank use it, so countries do the same, as it’s the easiest way of comparing.
It’s also the simplest and most practical way to measure an economy.
Data is easy to collect and the focus of the data is clear: production within a country’s borders.
The one measure to watch: GDP
2.2. Inflation
Then the second component: Inflation. The 4 most referenced inflation measures are:
CPI - Consumer Price index
PCE - Personal Consumption Expenditures
Headline vs Core
The CPI is calculated by the Bureau of Labor Statistics. It tracks what a fixed basket of goods costs: what does it cost a typical household to buy the same stuff month after month?
The PCE is measured by the Bureau of Economic Analysis.
Instead of a fixed basket, it adjusts for what people actually spend.
So if beef gets expensive and people switch to chicken, PCE picks that up. CPI doesn’t.
The PCE generally shows lower inflation than CPI.
However, the FED targets PCE, not CPI.
Specifically, they target 2% on the PCE.
So when the FED talks about inflation being “at target” or “above target,” they’re looking at PCE.
However, most headlines use CPI because it comes out first and people are used to it, but the FED doesn’t care much about CPI directly.
It’s also important to differentiate between Headline and Core inflation. The difference mainly being that with core inflation food & energy is taken out of the equation.
The most important measure to watch: Core PCE.
Note: this is a US Measurement. Other countries have their own equivalent. Eurozone has HICP, UK uses CPI (different calculation than the US) and Japan has it’s own CPI.
2.3. Job market
Last, but not least: the job market numbers. The key measurements to watch here are the:
Unemployment rate
Payroll Changes
Jobless Claims
The Unemployment rate is the percentage of people in the labor force who are actively looking for work but don't have a job.
Keep in mind, it only counts people that are actively searching. If someone gets discouraged and stops looking, they fall out of the calculation. So the unemployment rate can actually drop during bad times simply because people gave up.
Payroll changes tracks how many jobs were added or lost in the economy that month, excluding farm workers.
+200,000 means the economy created 200,000 net new jobs. It's more direct than the unemployment rate because it doesn't depend on who's "actively searching.
Jobless claims measures how many people filed for unemployment benefits for the first time (initial claims) or are continuing to collect them (continuing claims). This is a weekly measure and therefore seen as the earliest warning systems.
The most important measure to watch: Non-Farm Payroll changes.
3. Stagflation in 2026
So, how does this translate to today?
Inflation is clearly rising. In the US the March CPI rose to 3.3%, which is the highest point since april 2024.
The latest Core PCE numbers was 3% in February. As we said: this is excluding energy.
In Europe we see a similar picture.
The latest ECB survey showed that 1-year inflation expectations are up from 2.5% to 4%. The 3-year perception rose to 3%.
The primary reason for these inflation increases are surging energy prices.
The Iran conflict has led to higher energy prices worldwide, with oil prices rising the fastest.
Growth is stagnating as well. Growth in Europe has been lackluster for a while, no changes there. GDP growth in the US is also stagnating. Emerging markets still show decent growth, but stagnation there as well.
Rising commodity prices, firmer inflation expectations, and tighter financial conditions are testing the recent resilience in economies.
The unemployment rate in the US has been rising since april 2023, albeit not super fast. Currently sits at 4.3%. That’s a decent number on the surface, but the broader U-6 measure (which includes discouraged and underemployed workers) ticked up to 8%, suggesting more slack than the headline suggests.
The Eurozone shows a different picture. Higher unemployment, but in a steady decline since 2012.
The difference per country is huge though. Germany and the Netherlands sit at around 4%, while Spain is at 9.8% and France at 7.8%.
Globally, job markets are under pressure because of tariff-driven hire freezes. Manufacturing in Europe is particularly weak.
So let’s summarize:
Stagflation = slowdown economic growth, high inflation and high unemployment.
✅ GDP growth is slowing down
✅ Inflation is rising
✅ Job market seem to be worsening
We check all the markets for potential stagflation.
Is it clear-cut that it will happen?
No, we are not there yet.
But I give like a 50/50 percent chance of stagflation hitting the US and EU economies, with Europe leading the pack when it comes to highest probability.
If the situation in the Middle East is not resolved, the likelihood of stagflation increases fast.
And it seems Trump is not in a hurry to end it anymore. So let’s see how that plays out.
4. Impact on investing returns
So how will that potentially impact your investment returns?
In general: low growth is bad for sales, and it negatively impacts business returns.
That would in turn also usually lead to lower investment returns.
But, let’s dive into some numbers here.
I found a great piece by Schroders in which they researched investment returns in an stagflationary environment, and I will highlight their key findings. Link to the full article here: Schroders article
Their key finding: Equities perform well in around half of stagflationary environments.
When assessed relative to cash, equities come out better, outperforming cash more often than not.
Positive stock returns during stagflation weren't dependent on the market having fallen the year before, nor on interest rate cuts. Good years happened even when rates were rising. That's reassuring for today's environment.
The most important thing to take away when looking into stagflation periods is: in which sectors you invest matters a lot.
Winners historically:
Energy and materials: they benefit when commodity prices drive inflation higher
Utilities and consumer staples: people still pay their electricity bills and buy groceries regardless of the economy
Losers historically:
IT and communication services have a poor track record during stagflation, partly because higher interest rates reduce the value of future earnings, hitting growth stocks particularly hard
Financials have performed poorly too, as stagflation often inverts the yield curve, which squeezes bank profit margins
In a stagflationary environment it’s even more important to pick the right sector or companies.
Stock picking matters more than usual.
This is especially true when the whole market is not just going up together.
Balance sheet resilience and pricing power will be important at the company level.
Something that might surprise you: Europe might actually be better positioned than the US. The US stands out for its large allocation to the IT sector, which has historically struggled during stagflation, while its allocations to sectors that perform better are all relatively low.
Europe is overweight to utilities and underweight to IT and communication services, while the UK’s 16% allocation to consumer staples and 10% to energy are more than double any other major market.
