Offshore
Photo
Dimitry Nakhla | Babylon Capitalยฎ
Visa was JUST trading at a 4% FCF yield & legendary investor Chris Hohn increased his $V stake by +47%, making it 18% of TCI Fund ๐ต
Hereโs what $V has returned (CAGR %) each time it hit a 4% FCF yield for the first time in a given year since 2016
1. +17.8% CAGR | (1/19/16)
2. +16.2% CAGR | (9/27/17)
3. +15.6% CAGR | (2/8/18)
4. +12.2% CAGR | (8/5/19)
5. +15.6% CAGR | (3/16/20)
6. +17.1% CAGR | (3/7/22)
7. +18.0% CAGR | (9/21/23)
8. +13.9% CAGR | (4/24/24)
9. โ | (11/14/25)
___
๐๐๐๐๐๐๐๐๐๐โผ๏ธ
๐๐ก๐ข๐ฌ ๐๐จ๐ง๐ญ๐๐ง๐ญ ๐ข๐ฌ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ ๐๐จ๐ซ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐๐ง๐ ๐๐๐ฎ๐๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐ฉ๐ฎ๐ซ๐ฉ๐จ๐ฌ๐๐ฌ ๐จ๐ง๐ฅ๐ฒ ๐๐ง๐ ๐๐จ๐๐ฌ ๐ง๐จ๐ญ ๐๐จ๐ง๐ฌ๐ญ๐ข๐ญ๐ฎ๐ญ๐ ๐ข๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐, ๐๐ง ๐จ๐๐๐๐ซ, ๐จ๐ซ ๐ ๐ฌ๐จ๐ฅ๐ข๐๐ข๐ญ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐ฎ๐ฒ ๐จ๐ซ ๐ฌ๐๐ฅ๐ฅ ๐๐ง๐ฒ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ฒ.
๐๐๐๐ฒ๐ฅ๐จ๐ง ๐๐๐ฉ๐ข๐ญ๐๐ฅยฎ ๐๐ง๐ ๐ข๐ญ๐ฌ ๐ซ๐๐ฉ๐ซ๐๐ฌ๐๐ง๐ญ๐๐ญ๐ข๐ฏ๐๐ฌ ๐ฆ๐๐ฒ ๐ก๐จ๐ฅ๐ ๐ฉ๐จ๐ฌ๐ข๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ข๐๐ฌ ๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ๐๐. ๐๐ง๐ฒ ๐จ๐ฉ๐ข๐ง๐ข๐จ๐ง๐ฌ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐๐ ๐๐ซ๐ ๐๐ฌ ๐จ๐ ๐ญ๐ก๐ ๐๐๐ญ๐ ๐จ๐ ๐ฉ๐ฎ๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐ฌ๐ฎ๐๐ฃ๐๐๐ญ ๐ญ๐จ ๐๐ก๐๐ง๐ ๐ ๐ฐ๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ง๐จ๐ญ๐ข๐๐.
๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐ก๐๐ฌ ๐๐๐๐ง ๐จ๐๐ญ๐๐ข๐ง๐๐ ๐๐ซ๐จ๐ฆ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐๐๐ฅ๐ข๐๐ฏ๐๐ ๐ญ๐จ ๐๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐๐ฎ๐ญ ๐ข๐ฌ ๐ง๐จ๐ญ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐๐ ๐๐ฌ ๐ญ๐จ ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐จ๐ซ ๐๐จ๐ฆ๐ฉ๐ฅ๐๐ญ๐๐ง๐๐ฌ๐ฌ. ๐๐๐ฌ๐ญ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐จ๐๐ฌ ๐ง๐จ๐ญ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐ ๐๐ฎ๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ.
tweet
Visa was JUST trading at a 4% FCF yield & legendary investor Chris Hohn increased his $V stake by +47%, making it 18% of TCI Fund ๐ต
Hereโs what $V has returned (CAGR %) each time it hit a 4% FCF yield for the first time in a given year since 2016
1. +17.8% CAGR | (1/19/16)
2. +16.2% CAGR | (9/27/17)
3. +15.6% CAGR | (2/8/18)
4. +12.2% CAGR | (8/5/19)
5. +15.6% CAGR | (3/16/20)
6. +17.1% CAGR | (3/7/22)
7. +18.0% CAGR | (9/21/23)
8. +13.9% CAGR | (4/24/24)
9. โ | (11/14/25)
___
๐๐๐๐๐๐๐๐๐๐โผ๏ธ
๐๐ก๐ข๐ฌ ๐๐จ๐ง๐ญ๐๐ง๐ญ ๐ข๐ฌ ๐ฉ๐ซ๐จ๐ฏ๐ข๐๐๐ ๐๐จ๐ซ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐๐ง๐ ๐๐๐ฎ๐๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐ฉ๐ฎ๐ซ๐ฉ๐จ๐ฌ๐๐ฌ ๐จ๐ง๐ฅ๐ฒ ๐๐ง๐ ๐๐จ๐๐ฌ ๐ง๐จ๐ญ ๐๐จ๐ง๐ฌ๐ญ๐ข๐ญ๐ฎ๐ญ๐ ๐ข๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐, ๐๐ง ๐จ๐๐๐๐ซ, ๐จ๐ซ ๐ ๐ฌ๐จ๐ฅ๐ข๐๐ข๐ญ๐๐ญ๐ข๐จ๐ง ๐ญ๐จ ๐๐ฎ๐ฒ ๐จ๐ซ ๐ฌ๐๐ฅ๐ฅ ๐๐ง๐ฒ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ฒ.
๐๐๐๐ฒ๐ฅ๐จ๐ง ๐๐๐ฉ๐ข๐ญ๐๐ฅยฎ ๐๐ง๐ ๐ข๐ญ๐ฌ ๐ซ๐๐ฉ๐ซ๐๐ฌ๐๐ง๐ญ๐๐ญ๐ข๐ฏ๐๐ฌ ๐ฆ๐๐ฒ ๐ก๐จ๐ฅ๐ ๐ฉ๐จ๐ฌ๐ข๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ข๐๐ฌ ๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ๐๐. ๐๐ง๐ฒ ๐จ๐ฉ๐ข๐ง๐ข๐จ๐ง๐ฌ ๐๐ฑ๐ฉ๐ซ๐๐ฌ๐ฌ๐๐ ๐๐ซ๐ ๐๐ฌ ๐จ๐ ๐ญ๐ก๐ ๐๐๐ญ๐ ๐จ๐ ๐ฉ๐ฎ๐๐ฅ๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ง๐ ๐ฌ๐ฎ๐๐ฃ๐๐๐ญ ๐ญ๐จ ๐๐ก๐๐ง๐ ๐ ๐ฐ๐ข๐ญ๐ก๐จ๐ฎ๐ญ ๐ง๐จ๐ญ๐ข๐๐.
๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐ก๐๐ฌ ๐๐๐๐ง ๐จ๐๐ญ๐๐ข๐ง๐๐ ๐๐ซ๐จ๐ฆ ๐ฌ๐จ๐ฎ๐ซ๐๐๐ฌ ๐๐๐ฅ๐ข๐๐ฏ๐๐ ๐ญ๐จ ๐๐ ๐ซ๐๐ฅ๐ข๐๐๐ฅ๐ ๐๐ฎ๐ญ ๐ข๐ฌ ๐ง๐จ๐ญ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐๐ ๐๐ฌ ๐ญ๐จ ๐๐๐๐ฎ๐ซ๐๐๐ฒ ๐จ๐ซ ๐๐จ๐ฆ๐ฉ๐ฅ๐๐ญ๐๐ง๐๐ฌ๐ฌ. ๐๐๐ฌ๐ญ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐๐จ๐๐ฌ ๐ง๐จ๐ญ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐ ๐๐ฎ๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ.
tweet
Offshore
Photo
Fiscal.ai
Is Nubank the fastest growing bank in the world?
$629 million โ $38.8 billion in customer deposits in 7 years.
Outrageous growth.
$NU https://t.co/IZYeruehJ5
tweet
Is Nubank the fastest growing bank in the world?
$629 million โ $38.8 billion in customer deposits in 7 years.
Outrageous growth.
$NU https://t.co/IZYeruehJ5
tweet
Offshore
Photo
Fiscal.ai
ASML sells fewer than 500 units per year and generates $37 billion in revenue.
Is there any company in the world with a wider moat?
$ASML https://t.co/N8fSDsr6jT
tweet
ASML sells fewer than 500 units per year and generates $37 billion in revenue.
Is there any company in the world with a wider moat?
$ASML https://t.co/N8fSDsr6jT
tweet
Offshore
Photo
WealthyReadings
RT @WealthyReadings: ๐จ EVERYONE IS GETTING THE $META ร $GOOG ร $NVDA DRAMA WRONG
The reality isn't that $META chose TPUs.
The reality is that $META needs always more compute.
This situation should be the signal the market was looking for to accept that the AI trade and the compute supply constraint is real, for longer.
$META's been starving for compute for months. They built datacenters at full speed, took on debt, went to external providers, got rejected by $NBIS and probably other neoclouds.
$META cannot find compute.
And $NVDA's GPUs are getting longer to produce due to deeper personalization.
Payment delays + inventories do not translate demand issues, they translate supply chain constraints. GPUs just canโt be produced and customized fast enough.
So $META turning to $GOOG isnโt about โbetter infra.โ Itโs about $GOOG having an independent pipeline and possibly some capacity left or soon to be online, while others donโt.
This isnโt a loss for $NVDA. Itโs a symptom of overwhelming compute demand.
You'll find detailed content below to go further in all the concepts shared here. Everything is data backed.
tweet
RT @WealthyReadings: ๐จ EVERYONE IS GETTING THE $META ร $GOOG ร $NVDA DRAMA WRONG
The reality isn't that $META chose TPUs.
The reality is that $META needs always more compute.
This situation should be the signal the market was looking for to accept that the AI trade and the compute supply constraint is real, for longer.
$META's been starving for compute for months. They built datacenters at full speed, took on debt, went to external providers, got rejected by $NBIS and probably other neoclouds.
$META cannot find compute.
And $NVDA's GPUs are getting longer to produce due to deeper personalization.
Payment delays + inventories do not translate demand issues, they translate supply chain constraints. GPUs just canโt be produced and customized fast enough.
So $META turning to $GOOG isnโt about โbetter infra.โ Itโs about $GOOG having an independent pipeline and possibly some capacity left or soon to be online, while others donโt.
This isnโt a loss for $NVDA. Itโs a symptom of overwhelming compute demand.
You'll find detailed content below to go further in all the concepts shared here. Everything is data backed.
tweet
WealthyReadings
RT @mvcinvesting: I see many investors more focused on looking smart than actually making money.
Iโm not defending speculators who chase every hyped stock, but dismissing an opportunity just because it's not unknown while bragging about some fishy 3x PE stock isnโt the flex you think it is.
tweet
RT @mvcinvesting: I see many investors more focused on looking smart than actually making money.
Iโm not defending speculators who chase every hyped stock, but dismissing an opportunity just because it's not unknown while bragging about some fishy 3x PE stock isnโt the flex you think it is.
tweet
Offshore
Photo
Fiscal.ai
There are only 15 software companies in North America that have:
5yr Revenue CAGR: >30%
&
EV/Gross Profit: <5x https://t.co/v67SfYXf6j
tweet
There are only 15 software companies in North America that have:
5yr Revenue CAGR: >30%
&
EV/Gross Profit: <5x https://t.co/v67SfYXf6j
tweet
Offshore
Photo
EndGame Macro
A High Without Momentum And The Real Story Behind the Median Income Chart
Once you dig into the Census Bureauโs own release, the story shifts. Theyโre very clear that the 2024 median income isnโt statistically different from 2023 or even from 2019. In plain English, that means the typical household today is standing in basically the same spot it was before Covid, once you adjust for inflation. The bump on the chart looks meaningful, but the data itself doesnโt support that kind of victory lap.
The Lived Experience Doesnโt Line Up
Even if you accept the number at face value, the lived reality for most families tells you something else. Housing, childcare, groceries, insuranceโฆthose are the bills people canโt avoid, and theyโve risen much faster than the broad inflation adjustment used in the headline number. So a flat median income ends up feeling like a step backward. People arenโt imagining the squeeze. When the Census report notes that wage gains were effectively erased by higher costs, it lines up with what you hear from almost anyone trying to balance a family budget right now.
The Median Masks Uneven Ground
The other issue is that a single median number glosses over enormous differences across groups. The Census data show that Hispanic and Asian households made real progress, while Black households actually saw a decline and White households saw no statistically meaningful change. Only Hispanic households are clearly better off today than they were in 2019. That means the overall number is being pulled upward by a few pockets of improvement while others are barely hanging on. Itโs a national average stitched together from very different economic realities.
Data Footnotes Matter More Than People Think
There are also technical quirks youโd never know from looking at the chart. The Census updated its migration estimates and flagged increased nonresponse bias for Hispanic households, which means some of the reported gains might reflect who answered the survey, not just actual economic improvement. This doesnโt invalidate the data, but it does mean you shouldnโt use it as proof that American households broadly surged ahead.
The Real Story Beneath the Headline
When you put all of this together, the picture is much more grounded. Household income isnโt collapsing, but itโs not breaking new ground either. Most families are dealing with higher costs that eat away at whatever nominal progress they see. And the small gains that do exist are concentrated in certain groups, not shared across the board. So the headline isnโt technically false, itโs just incomplete. It stretches a flat, uneven reality into something that sounds like economic momentum. And thatโs why it doesnโt resonate with what people feel in their own lives.
tweet
A High Without Momentum And The Real Story Behind the Median Income Chart
Once you dig into the Census Bureauโs own release, the story shifts. Theyโre very clear that the 2024 median income isnโt statistically different from 2023 or even from 2019. In plain English, that means the typical household today is standing in basically the same spot it was before Covid, once you adjust for inflation. The bump on the chart looks meaningful, but the data itself doesnโt support that kind of victory lap.
The Lived Experience Doesnโt Line Up
Even if you accept the number at face value, the lived reality for most families tells you something else. Housing, childcare, groceries, insuranceโฆthose are the bills people canโt avoid, and theyโve risen much faster than the broad inflation adjustment used in the headline number. So a flat median income ends up feeling like a step backward. People arenโt imagining the squeeze. When the Census report notes that wage gains were effectively erased by higher costs, it lines up with what you hear from almost anyone trying to balance a family budget right now.
The Median Masks Uneven Ground
The other issue is that a single median number glosses over enormous differences across groups. The Census data show that Hispanic and Asian households made real progress, while Black households actually saw a decline and White households saw no statistically meaningful change. Only Hispanic households are clearly better off today than they were in 2019. That means the overall number is being pulled upward by a few pockets of improvement while others are barely hanging on. Itโs a national average stitched together from very different economic realities.
Data Footnotes Matter More Than People Think
There are also technical quirks youโd never know from looking at the chart. The Census updated its migration estimates and flagged increased nonresponse bias for Hispanic households, which means some of the reported gains might reflect who answered the survey, not just actual economic improvement. This doesnโt invalidate the data, but it does mean you shouldnโt use it as proof that American households broadly surged ahead.
The Real Story Beneath the Headline
When you put all of this together, the picture is much more grounded. Household income isnโt collapsing, but itโs not breaking new ground either. Most families are dealing with higher costs that eat away at whatever nominal progress they see. And the small gains that do exist are concentrated in certain groups, not shared across the board. So the headline isnโt technically false, itโs just incomplete. It stretches a flat, uneven reality into something that sounds like economic momentum. And thatโs why it doesnโt resonate with what people feel in their own lives.
Adjusted for inflation, the median household income for Americans in 2024 was *the highest itโs ever been* https://t.co/qMesQvJyx0 tweet
Offshore
Photo
EndGame Macro
Cyclical America Is Quietly Rolling Over And The Rest Will Follow
The labor market doesnโt weaken all at once. It breaks in sequence. The most interest rate sensitive jobs crack first, and everything else follows with a lag. And when you look at these two panels side by side, you can see that sequence playing out almost in real time.
Construction and manufacturing payrolls have clearly rolled over. They peaked, wobbled, and then pushed lower. Thatโs exactly what @EPBResearch describes in the early chapters of the cycleโฆthe first signs of stress always show up in the sectors that live closest to credit conditions. Residential construction and durable goods manufacturing the most cyclical slice have led every downturn of the last few decades.
Meanwhile, non cyclical payrolls are still grinding higher like nothingโs wrong. Thatโs the part that makes the headline numbers so misleading. When you blend both sides together, the total payroll figure still looks healthy because the slower moving sectors (healthcare, education, government linked services) donโt react until much later. By the time they start weakening, the process is already well underway under the surface.
My Read on What This Means
To me, this split tells a very straightforward storyโฆthe labor market isnโt collapsing, but the foundation has already started to soften. Construction and manufacturing turning down is the predictable after effect of two years of tight money. When cyclical employment slows, the unemployment rate drifts up, full time work gets replaced by part time work, and only then does the broader labor market begin to feel it.
Weโre now somewhere in the middle of that sequence. The unemployment rate has already pushed above 4.4%, full time share of employment has slipped under 79%, and part time for economic reasons has ticked higher. None of that screams recession by itself but taken together, it paints a picture of momentum thatโs fading, not stabilizing.
So while the aggregate payrolls still look fine on the surface, the leading edge is already bending. And once the cyclical side turns, history says the rest of the labor market eventually follows. The real question now isnโt whether the slowdown is happening, the charts already answer that. The question is whether the Fed has and will ease quickly enough to keep this from spilling over into the non cyclical sectors. Thatโs the hinge point for the next few quarters.
In other words, if you want to know where the labor market is heading, donโt watch the big headline on jobs day. Watch these two charts next to each other. Theyโll tell you the story long before the monthly payroll number does.
tweet
Cyclical America Is Quietly Rolling Over And The Rest Will Follow
The labor market doesnโt weaken all at once. It breaks in sequence. The most interest rate sensitive jobs crack first, and everything else follows with a lag. And when you look at these two panels side by side, you can see that sequence playing out almost in real time.
Construction and manufacturing payrolls have clearly rolled over. They peaked, wobbled, and then pushed lower. Thatโs exactly what @EPBResearch describes in the early chapters of the cycleโฆthe first signs of stress always show up in the sectors that live closest to credit conditions. Residential construction and durable goods manufacturing the most cyclical slice have led every downturn of the last few decades.
Meanwhile, non cyclical payrolls are still grinding higher like nothingโs wrong. Thatโs the part that makes the headline numbers so misleading. When you blend both sides together, the total payroll figure still looks healthy because the slower moving sectors (healthcare, education, government linked services) donโt react until much later. By the time they start weakening, the process is already well underway under the surface.
My Read on What This Means
To me, this split tells a very straightforward storyโฆthe labor market isnโt collapsing, but the foundation has already started to soften. Construction and manufacturing turning down is the predictable after effect of two years of tight money. When cyclical employment slows, the unemployment rate drifts up, full time work gets replaced by part time work, and only then does the broader labor market begin to feel it.
Weโre now somewhere in the middle of that sequence. The unemployment rate has already pushed above 4.4%, full time share of employment has slipped under 79%, and part time for economic reasons has ticked higher. None of that screams recession by itself but taken together, it paints a picture of momentum thatโs fading, not stabilizing.
So while the aggregate payrolls still look fine on the surface, the leading edge is already bending. And once the cyclical side turns, history says the rest of the labor market eventually follows. The real question now isnโt whether the slowdown is happening, the charts already answer that. The question is whether the Fed has and will ease quickly enough to keep this from spilling over into the non cyclical sectors. Thatโs the hinge point for the next few quarters.
In other words, if you want to know where the labor market is heading, donโt watch the big headline on jobs day. Watch these two charts next to each other. Theyโll tell you the story long before the monthly payroll number does.
Cyclical vs. Non-Cyclical Payrolls
Itโs always most important to analyze the health of construction & manufacturing because you can spot potential inflection points first, as they occur in these sectors before bleeding into the aggregate statistics.
https://t.co/9OWAVVQlDG https://t.co/DZoXI9RybF tweet
Clark Square Capital
This is sooo good.
LONG POSITION SIZING: PART 2
Investors generally spend a disproportionate time and mental energy seeking to quantify position level up/down and financial projections, while often using little analytical rigor in position sizing, which also materially drives returns. This is the premise behind @alpha_theory. An (updated)๐งต:
1. Summary
2. Background
3. Framework
4. Example
5. Conclusion
1. SUMMARY
At my prior fund, we historically sized positions based on a combination of our conviction and how much we thought we could lose if we were wrong. I copied this approach when I went out on my own, but layered in additional criteria adjust for factors that make a position inherently more risky or uncertain - examples include dinging sizing for companies that are burning cash, have substantial leverage, or where the margin for error in our assessment is likely to be wide.
After 1.5yrs of collecting data based on how I classified each investment at position inception and subsequent results, I believe I am able to refine my approach to sizing with a better appreciation for which factors were most correlated with good/bad outcomes, and in this way hope to improve sizing logic so we make more on our winners and less on our losers over time. While this approach isnโt perfect, I believe itโs better than sizing based purely on emotion, instinct, and memory, all of which are faulty and subject to cognitive biases. Itโs also much harder to refine your approach to sizing if the inputs to that initial decison were all done in your head.
2. POSITION SIZING - BACKGROUND
I worked at 4 funds before starting my own. At 2, sizing was a block box - I either didnโt know our position sizes or never heard how the PM thought about sizing. At the two where sizing was discussed, at one it was based on a loose feeling of conviction, irr, and max position size. At the second, there was a concept of value at risk (not wanting to lose more than a certain amount) and conviction in sizing, but position sizes tended to cluster all at the same level, about 60% below max position size, and the conceptual discipline wasnโt always enforced in practice.
At all 4, the amount of time we spent thinking about how to size positions was a fraction of the time we spent on other activities. And while there was a loose framework with all of them, sizing ultimately came down to the pm and or analyst having a feeling this was a very good idea and should be sized close to max. Iโm not sure this is necessarily a bad thing - thereโs something to be said about intuition, especially where the PM has a history of having very good intuition for the best ideas (my prior fund). But intuition leaves no record of thought process to be improved upon, is hard to impart to analysts without decision making authority, and changes based on mood, recent experience and cognitive biases not founded in logic.
@Alpha_Theory is great software, but many of its basic principles can be copied without using it. At its core, the premise is that you can break down the elements of โjudgmentโ and โintuitionโ into variables that you are forced to record at the time of your initial investment and change over time as the position matures. This has a few benefits over an intuitive approach:
1) it enforce a discipline around why you size things the way you do and is less prone to emotion based decision making.
2) It also allows analysts to - who are often closer to the work - to contribute more to sizing because they can help inform your variables. Itโs hard for them to have influence when the PM hides behind just their โjudgmentโ
3) it allows for a fact based assessment in retrospect around where mistakes in judgment or position sizing were made, that can be incorporated into future sizing decisions. If you have robust systems, you can also take this data and put it into a tool like Lightkeeper to see which variables were most associated with the best or worst outcomes. - HF Reflections tweet
This is sooo good.
LONG POSITION SIZING: PART 2
Investors generally spend a disproportionate time and mental energy seeking to quantify position level up/down and financial projections, while often using little analytical rigor in position sizing, which also materially drives returns. This is the premise behind @alpha_theory. An (updated)๐งต:
1. Summary
2. Background
3. Framework
4. Example
5. Conclusion
1. SUMMARY
At my prior fund, we historically sized positions based on a combination of our conviction and how much we thought we could lose if we were wrong. I copied this approach when I went out on my own, but layered in additional criteria adjust for factors that make a position inherently more risky or uncertain - examples include dinging sizing for companies that are burning cash, have substantial leverage, or where the margin for error in our assessment is likely to be wide.
After 1.5yrs of collecting data based on how I classified each investment at position inception and subsequent results, I believe I am able to refine my approach to sizing with a better appreciation for which factors were most correlated with good/bad outcomes, and in this way hope to improve sizing logic so we make more on our winners and less on our losers over time. While this approach isnโt perfect, I believe itโs better than sizing based purely on emotion, instinct, and memory, all of which are faulty and subject to cognitive biases. Itโs also much harder to refine your approach to sizing if the inputs to that initial decison were all done in your head.
2. POSITION SIZING - BACKGROUND
I worked at 4 funds before starting my own. At 2, sizing was a block box - I either didnโt know our position sizes or never heard how the PM thought about sizing. At the two where sizing was discussed, at one it was based on a loose feeling of conviction, irr, and max position size. At the second, there was a concept of value at risk (not wanting to lose more than a certain amount) and conviction in sizing, but position sizes tended to cluster all at the same level, about 60% below max position size, and the conceptual discipline wasnโt always enforced in practice.
At all 4, the amount of time we spent thinking about how to size positions was a fraction of the time we spent on other activities. And while there was a loose framework with all of them, sizing ultimately came down to the pm and or analyst having a feeling this was a very good idea and should be sized close to max. Iโm not sure this is necessarily a bad thing - thereโs something to be said about intuition, especially where the PM has a history of having very good intuition for the best ideas (my prior fund). But intuition leaves no record of thought process to be improved upon, is hard to impart to analysts without decision making authority, and changes based on mood, recent experience and cognitive biases not founded in logic.
@Alpha_Theory is great software, but many of its basic principles can be copied without using it. At its core, the premise is that you can break down the elements of โjudgmentโ and โintuitionโ into variables that you are forced to record at the time of your initial investment and change over time as the position matures. This has a few benefits over an intuitive approach:
1) it enforce a discipline around why you size things the way you do and is less prone to emotion based decision making.
2) It also allows analysts to - who are often closer to the work - to contribute more to sizing because they can help inform your variables. Itโs hard for them to have influence when the PM hides behind just their โjudgmentโ
3) it allows for a fact based assessment in retrospect around where mistakes in judgment or position sizing were made, that can be incorporated into future sizing decisions. If you have robust systems, you can also take this data and put it into a tool like Lightkeeper to see which variables were most associated with the best or worst outcomes. - HF Reflections tweet
Offshore
Photo
EndGame Macro
Why EM Is Up Big And Why the Story Isnโt as Simple as It Looks
If you only look at the year to date column, it feels like EM suddenly woke upโฆup around 30%, Europe and Japan small caps right behind, and the U.S. no longer carrying global equities. But when you look across the other columns, a clearer picture emerges. EM has dominated the 6 and 12 month windows, but itโs one of the weakest performers over the last month. Thatโs what a front loaded rally looks like, a big move that started earlier in the cycle and is now running into some fatigue.
And when you line that up with policy, it actually makes perfect sense. The Fed didnโt wait until late 2025 to turn; the real shift started in late 2024 with the three rate cuts in September, November, and December. Thatโs also when the dollar rolled over from the 110s back toward the high 90s. It didnโt collapse, but it broke its uptrend. That change in tone set the stage for everything that followed.
Why the Move Started Before 2025 Even Began
By January, the market wasnโt operating in higher for longer anymore even if policymakers werenโt ready to say it out loud. Inflation had cooled, long yields looked like they had topped, and the Fed had already eased a full percentage point off the peak. Global investors went into 2025 massively overweight U.S. mega caps and massively underweight everything else. Thatโs all you need to ignite a rotation.
Once the dollar stopped rising and the Fed stopped tightening, EM and foreign small caps instantly looked like the only parts of the world that hadnโt already been bid to the moon. And unlike past cycles, there were real local stories this timeโฆIndiaโs tech and services boom, Mexicoโs near shoring wave, Southeast Asia picking up supply chain flows, commodity economies finally catching a bid after a decade of underinvestment. Those arenโt just macro trades, theyโre genuine growth narratives.
So the early 2025 surge was the combination of relief, cheap valuations, and real idiosyncratic strength. The cuts in September and October 2025 didnโt cause the move they just extended the backdrop that allowed it.
Where This Actually Leaves Us
The important nuance is that none of this means global dollar conditions have suddenly become easy. A strong EM equity year doesnโt erase the fact that countries like Argentina still needed massive swap line and IMF support, or that euro area banks remain deeply exposed to short term dollar funding. Equity markets can look great even while the dollar plumbing underneath is still stressed. Thatโs the difference between the surface story and the structural one.
My Read
2025 was the catch up phase, the global portfolio unwind after a decade of everything flowing into the same handful of U.S. names. The big move happened once the Fed turned in 2024 and the dollar broke its trend. From here, the winners wonโt be EM in the broad sense. Theyโll be the countries with real engines behind them with near shoring, AI and semiconductor supply chains, energy and metals investment, political stability rather than the ones that rallied just because they were neglected.
The table tells you who benefitted from the initial rotation. The harder question, and the one that matters now, is who can actually keep climbing once the easy part is over.
tweet
Why EM Is Up Big And Why the Story Isnโt as Simple as It Looks
If you only look at the year to date column, it feels like EM suddenly woke upโฆup around 30%, Europe and Japan small caps right behind, and the U.S. no longer carrying global equities. But when you look across the other columns, a clearer picture emerges. EM has dominated the 6 and 12 month windows, but itโs one of the weakest performers over the last month. Thatโs what a front loaded rally looks like, a big move that started earlier in the cycle and is now running into some fatigue.
And when you line that up with policy, it actually makes perfect sense. The Fed didnโt wait until late 2025 to turn; the real shift started in late 2024 with the three rate cuts in September, November, and December. Thatโs also when the dollar rolled over from the 110s back toward the high 90s. It didnโt collapse, but it broke its uptrend. That change in tone set the stage for everything that followed.
Why the Move Started Before 2025 Even Began
By January, the market wasnโt operating in higher for longer anymore even if policymakers werenโt ready to say it out loud. Inflation had cooled, long yields looked like they had topped, and the Fed had already eased a full percentage point off the peak. Global investors went into 2025 massively overweight U.S. mega caps and massively underweight everything else. Thatโs all you need to ignite a rotation.
Once the dollar stopped rising and the Fed stopped tightening, EM and foreign small caps instantly looked like the only parts of the world that hadnโt already been bid to the moon. And unlike past cycles, there were real local stories this timeโฆIndiaโs tech and services boom, Mexicoโs near shoring wave, Southeast Asia picking up supply chain flows, commodity economies finally catching a bid after a decade of underinvestment. Those arenโt just macro trades, theyโre genuine growth narratives.
So the early 2025 surge was the combination of relief, cheap valuations, and real idiosyncratic strength. The cuts in September and October 2025 didnโt cause the move they just extended the backdrop that allowed it.
Where This Actually Leaves Us
The important nuance is that none of this means global dollar conditions have suddenly become easy. A strong EM equity year doesnโt erase the fact that countries like Argentina still needed massive swap line and IMF support, or that euro area banks remain deeply exposed to short term dollar funding. Equity markets can look great even while the dollar plumbing underneath is still stressed. Thatโs the difference between the surface story and the structural one.
My Read
2025 was the catch up phase, the global portfolio unwind after a decade of everything flowing into the same handful of U.S. names. The big move happened once the Fed turned in 2024 and the dollar broke its trend. From here, the winners wonโt be EM in the broad sense. Theyโll be the countries with real engines behind them with near shoring, AI and semiconductor supply chains, energy and metals investment, political stability rather than the ones that rallied just because they were neglected.
The table tells you who benefitted from the initial rotation. The harder question, and the one that matters now, is who can actually keep climbing once the easy part is over.
Emerging markets now +30.01% YTD
ex-US small caps very strong in 2025 https://t.co/6XpGtfjJLb tweet