Offshore
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โ Hidden Value Gems
Looks like alcohol stocks are following Tobacco, although they still trade at much higher premium. https://t.co/WKi1f7GIIV
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Looks like alcohol stocks are following Tobacco, although they still trade at much higher premium. https://t.co/WKi1f7GIIV
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Offshore
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โ Stock Analysis Compilation
Royal London AM on Niox $NIOX LN
Thesis: Niox's cutting-edge asthma diagnostics and recurring revenue model make it a scalable, cash-rich Medtech leader poised for growth
(Extract from their Q3 letter) https://t.co/qRnK0ibepS
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Royal London AM on Niox $NIOX LN
Thesis: Niox's cutting-edge asthma diagnostics and recurring revenue model make it a scalable, cash-rich Medtech leader poised for growth
(Extract from their Q3 letter) https://t.co/qRnK0ibepS
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Offshore
Video
โ Startup Archive
Stanley Tang shares three lessons from the founding story of DoorDash
Stanley Tang, Tony Xu, and Andy Fang founded DoorDash while they were students at Stanford.
In todayโs clip, Stanley tells the founding story of DoorDash and the three startup lessons he learned from this time.
#1 Test your hypothesis
During his junior year at Stanford, Stanley wanted to build technology for small business owners so he sat down with the owner of a macaroon store in Palo Alto to learn about her problems.
The owner took out a large booklet with pages of delivery orders that she had to turn down because she couldnโt fulfill them. This seemed like an interesting problem to the DoorDash founders so they spent the next few weeks talking to another 150-200 small businesses who also didnโt have a good solution for delivery.
This led the founders to wonder:
โDelivery is such an obvious thing. Why hasnโt anyone solved this before? We must be missing something. Maybe people have tried this in the past but failed because there wasnโt consumer demand. So we thought, okay, how can we test this hypothesis?โ
They decided to create a simple experiment and spent an afternoon putting together a quick landing page with some PDF menus of local restaurants in Palo Alto with their personal cell phone number at the bottom.
They called the company Palo Alto Delivery and werenโt expecting much:
โWe werenโt really expecting anything. We just launched it, and all we wanted to see was would we get phone calls from this? If we got enough phone calls then maybe this delivery idea was something worth pursuing.โ
Later that day, the founders received a call from their first customer. They picked up the Thai food order and delivered it themselves.
The next day they got two phone calls. Then five the day after that. Then seven. Then ten.
โSoon we started gaining traction on campus with Palo Alto Delivery, which was pretty crazy because it was just a landing page, you had to look up PDF menus to place your order, and then you had to call inโฆ Thatโs kind of when we knew we were onto something.โ
#2 Launch fast
As Stanley explains:
โI think another key point to remember is that we launched this in about an hour. We didnโt have any drivers. We didnโt have any algorithms. We didnโt spend six months building a fancy dispatch systemโฆ At the beginning, itโs all about testing your idea, trying to get this thing off the ground, and figuring out whether this was something people even wanted.โ
#3 Do things that donโt scale
โAt YC thereโs this mantra we like to talk about: โDoing things that donโt scale.โ At the beginning, we were the delivery drivers. We were customer supportโฆ We used Square to charge our customers. We used Google Docs to keep track of our orders. We used Appleโs Find My Friends to keep track of where all our drivers were. Just hacking together solutions just trying to get this thing off the ground.โ
Stanley continues:
โAnother thing about doing things that donโt scale is that it also allows you to become an expert in your business. Driving helped us understand how the delivery process worked. We used that as an opportunity to talk to our customers and restaurants. We manually dispatched every driver, and that helped us figure out what our driver assignment algorithm should look likeโฆ Now of course, weโve scaled across different cities and have to worry about building out automated solutions and dispatch systems and figuring out how to match demand and supply, and all that fancy technology stuff. But none of that matters in the beginning. At the beginning, itโs all about getting this thing off the ground and trying to find product/market fit.โ
In the first few months, the founders also manually emailed every new customer to ask how their fir[...]
Stanley Tang shares three lessons from the founding story of DoorDash
Stanley Tang, Tony Xu, and Andy Fang founded DoorDash while they were students at Stanford.
In todayโs clip, Stanley tells the founding story of DoorDash and the three startup lessons he learned from this time.
#1 Test your hypothesis
During his junior year at Stanford, Stanley wanted to build technology for small business owners so he sat down with the owner of a macaroon store in Palo Alto to learn about her problems.
The owner took out a large booklet with pages of delivery orders that she had to turn down because she couldnโt fulfill them. This seemed like an interesting problem to the DoorDash founders so they spent the next few weeks talking to another 150-200 small businesses who also didnโt have a good solution for delivery.
This led the founders to wonder:
โDelivery is such an obvious thing. Why hasnโt anyone solved this before? We must be missing something. Maybe people have tried this in the past but failed because there wasnโt consumer demand. So we thought, okay, how can we test this hypothesis?โ
They decided to create a simple experiment and spent an afternoon putting together a quick landing page with some PDF menus of local restaurants in Palo Alto with their personal cell phone number at the bottom.
They called the company Palo Alto Delivery and werenโt expecting much:
โWe werenโt really expecting anything. We just launched it, and all we wanted to see was would we get phone calls from this? If we got enough phone calls then maybe this delivery idea was something worth pursuing.โ
Later that day, the founders received a call from their first customer. They picked up the Thai food order and delivered it themselves.
The next day they got two phone calls. Then five the day after that. Then seven. Then ten.
โSoon we started gaining traction on campus with Palo Alto Delivery, which was pretty crazy because it was just a landing page, you had to look up PDF menus to place your order, and then you had to call inโฆ Thatโs kind of when we knew we were onto something.โ
#2 Launch fast
As Stanley explains:
โI think another key point to remember is that we launched this in about an hour. We didnโt have any drivers. We didnโt have any algorithms. We didnโt spend six months building a fancy dispatch systemโฆ At the beginning, itโs all about testing your idea, trying to get this thing off the ground, and figuring out whether this was something people even wanted.โ
#3 Do things that donโt scale
โAt YC thereโs this mantra we like to talk about: โDoing things that donโt scale.โ At the beginning, we were the delivery drivers. We were customer supportโฆ We used Square to charge our customers. We used Google Docs to keep track of our orders. We used Appleโs Find My Friends to keep track of where all our drivers were. Just hacking together solutions just trying to get this thing off the ground.โ
Stanley continues:
โAnother thing about doing things that donโt scale is that it also allows you to become an expert in your business. Driving helped us understand how the delivery process worked. We used that as an opportunity to talk to our customers and restaurants. We manually dispatched every driver, and that helped us figure out what our driver assignment algorithm should look likeโฆ Now of course, weโve scaled across different cities and have to worry about building out automated solutions and dispatch systems and figuring out how to match demand and supply, and all that fancy technology stuff. But none of that matters in the beginning. At the beginning, itโs all about getting this thing off the ground and trying to find product/market fit.โ
In the first few months, the founders also manually emailed every new customer to ask how their fir[...]
Offshore
โ Startup Archive Stanley Tang shares three lessons from the founding story of DoorDash Stanley Tang, Tony Xu, and Andy Fang founded DoorDash while they were students at Stanford. In todayโs clip, Stanley tells the founding story of DoorDash and the threeโฆ
st delivery went:
โFeedback like that was really valuable and our customers really appreciated thatโฆ Doing things that donโt scale is one of your biggest competitive advantages when youโre starting out. You can figure out how to scale once you have demand.โ
Video source: @ycombinator (2014)
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โFeedback like that was really valuable and our customers really appreciated thatโฆ Doing things that donโt scale is one of your biggest competitive advantages when youโre starting out. You can figure out how to scale once you have demand.โ
Video source: @ycombinator (2014)
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Offshore
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โ Stock Analysis Compilation
Polen Capital on TopBuild $BLD US
Thesis: TopBuild capitalizes on housing tailwinds with impressive margins and EPS growthโuncover why this insulation leader is a standout performer.
(Extract from their Q3 letter) https://t.co/22ZriJ4Y9Q
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Polen Capital on TopBuild $BLD US
Thesis: TopBuild capitalizes on housing tailwinds with impressive margins and EPS growthโuncover why this insulation leader is a standout performer.
(Extract from their Q3 letter) https://t.co/22ZriJ4Y9Q
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Offshore
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โ Investing visuals
Interesting: $PLTR's revenue per customer is steadily declining. Anyone have insights on why this might be? https://t.co/scTnNoxjHx
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Interesting: $PLTR's revenue per customer is steadily declining. Anyone have insights on why this might be? https://t.co/scTnNoxjHx
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Offshore
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โ Dimitry Nakhla | Babylon Capitalยฎ
A quality valuation analysis on $GOOG ๐ง๐ฝโโ๏ธ
โขNTM P/E Ratio: 22.47x
โข10-Year Mean: 23.65x
โขNTM FCF Yield: 3.90%
โข10-Year Mean: 4.18%
As you can see, $GOOG appears to be trading near fair value
Going forward, investors can receive ~5% MORE in earnings per share & ~6% LESS in FCF per share ๐ง ***
Before we get into valuation, letโs take a look at why $GOOG is a great business
BALANCE SHEETโ
โขCash & Short-Term Inv: $93.23B
โขLong-Term Debt: $10.88B
$GOOG has a strong balance sheet, an AA+ S&P Credit Rating & 370x FFO Interest Coverage
RETURN ON CAPITALโ
โข2019: 16.4%
โข2020: 16.2%
โข2021: 27.6%
โข2022: 26.1%
โข2023: 28.1%
โขLTM: 31.7%
RETURN ON EQUITYโ
โข2019: 18.1%
โข2020: 19.0%
โข2021: 32.1%
โข2022: 23.6%
โข2023: 27.4%
โขLTM: 32.1%
$GOOG has strong return metrics, highlighting the financial efficiency of the business
REVENUESโ
โข2018: $136.82B
โข2023: $307.39
โขCAGR: 17.57%
FREE CASH FLOWโ
โข2018: $22.83B
โข2023: $69.50B
โขCAGR: 24.93%
NORMALIZED EPSโ
โข2018: $2.19
โข2023: $5.80
โขCAGR: 21.50%
SHARE BUYBACKSโ
โข2018 Shares Outstanding: 14.07B
โขLTM Shares Outstanding: 12.51B
By reducing its shares outstanding ~11%, $GOOG increased its EPS by ~12.3% (assuming 0 growth)
MARGINSโ
โขLTM Gross Margins: 58.1%
โขLTM Operating Margins: 32.1%
โขLTM Net Income Margins: 27.7%
***NOW TO VALUATION ๐ง
As stated above, investors can expect to receive ~5% MORE in EPS & ~6% LESS in FCF per share
Using Benjamin Grahamโs 2G rule of thumb, $GOOG has to grow earnings at an 11.24% CAGR over the next several years to justify its valuation
Today, analysts anticipate 2025 - 2027 EPS growth over the next few years to be more than the (11.24%) required growth rate:
2024E: $7.98 (37.5% YoY) *FY Dec
2025E: $8.95 (12.2% YoY)
2026E: $10.19 (13.8% YoY)
2027E: $11.78 (15.6% YoY)
$GOOG has an excellent track record of meeting analyst estimates ~2 years out, so letโs assume $GOOG ends 2027 with $11.78 in EPS & see its CAGR potential assuming different multiples
24x P/E: $282.72๐ต โฆ ~14.0% CAGR
23x P/E: $270.94๐ต โฆ ~12.4% CAGR
22x P/E: $259.16๐ต โฆ ~10.7% CAGR
21x P/E: $247.38๐ต โฆ ~9.0% CAGR
20x P/E: $235.60๐ต โฆ ~7.3% CAGR
As you can see, $GOOG appears to have attractive return potential IF we assume >22x earnings (a multiple below its 5-year & 10-year mean)
At >24x earnings, $GOOG has aggressive CAGR potential & itโs not unreasonable for the business to even trade for ~24x (given its growth rate, moat, balance sheet, & exemplary capital allocation)
Those buying $GOOG today at $191๐ต are getting a great business at a more than reasonable price, ensuring a slight margin of safety
Between cloud โ๏ธ , AI ๐ค , quantum computing โ๏ธ, $GOOG has a strong growth runway ahead
I consider $GOOG a strong buy with a substantial margin of safety closer to $179๐ต where I could conservatively expect double-digit CAGR while assuming a 20x multiple
$GOOGL
___
๐๐๐๐๐๐๐๐๐๐โผ๏ธ: ๐๐ก๐ข๐ฌ ๐ข๐ฌ ๐๐๐ ๐๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐. ๐๐๐๐ฒ๐ฅ๐จ๐ง ๐๐๐ฉ๐ข๐ญ๐๐ฅยฎ ๐๐ง๐ ๐ข๐ญ๐ฌ ๐ซ๐๐ฉ๐ซ๐๐ฌ๐๐ง๐ญ๐๐ญ๐ข๐ฏ๐๐ฌ ๐ฆ๐๐ฒ ๐ก๐๐ฏ๐ ๐ฉ๐จ๐ฌ๐ข๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ข๐๐ฌ ๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ.
๐๐ก๐ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐๐ข๐ง๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ ๐ข๐ฌ ๐ข๐ง๐ญ๐๐ง๐๐๐ ๐๐จ๐ซ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐ฉ๐ฎ๐ซ๐ฉ๐จ๐ฌ๐๐ฌ ๐จ๐ง๐ฅ๐ฒ ๐๐ง๐ ๐ฌ๐ก๐จ๐ฎ๐ฅ๐ ๐ง๐จ๐ญ ๐๐ ๐๐จ๐ง๐ฌ๐ญ๐ซ๐ฎ๐๐ ๐๐ฌ ๐ข๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐ ๐ญ๐จ ๐ฆ๐๐๐ญ ๐ญ๐ก๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐ ๐ง๐๐๐๐ฌ ๐จ๐ ๐๐ง๐ฒ ๐ข๐ง๐๐ข๐ฏ๐ข๐๐ฎ๐๐ฅ ๐จ๐ซ ๐ฌ๐ข๐ญ๐ฎ๐๐ญ๐ข๐จ๐ง. ๐๐๐ฌ๐ญ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐ข๐ฌ ๐ง๐จ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐ ๐จ๐ ๐๐ฎ๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ.
๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐๐ข๐ง๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ ๐ก๐๐ฌ ๐๐๐๐ง ๐จ๐๐ญ๐๐ข๐ง๐๐ ๐๐ซ๐จ๐ฆ ๐ฌ๐จ๐ฎ๐ซ๏ฟฝ[...]
A quality valuation analysis on $GOOG ๐ง๐ฝโโ๏ธ
โขNTM P/E Ratio: 22.47x
โข10-Year Mean: 23.65x
โขNTM FCF Yield: 3.90%
โข10-Year Mean: 4.18%
As you can see, $GOOG appears to be trading near fair value
Going forward, investors can receive ~5% MORE in earnings per share & ~6% LESS in FCF per share ๐ง ***
Before we get into valuation, letโs take a look at why $GOOG is a great business
BALANCE SHEETโ
โขCash & Short-Term Inv: $93.23B
โขLong-Term Debt: $10.88B
$GOOG has a strong balance sheet, an AA+ S&P Credit Rating & 370x FFO Interest Coverage
RETURN ON CAPITALโ
โข2019: 16.4%
โข2020: 16.2%
โข2021: 27.6%
โข2022: 26.1%
โข2023: 28.1%
โขLTM: 31.7%
RETURN ON EQUITYโ
โข2019: 18.1%
โข2020: 19.0%
โข2021: 32.1%
โข2022: 23.6%
โข2023: 27.4%
โขLTM: 32.1%
$GOOG has strong return metrics, highlighting the financial efficiency of the business
REVENUESโ
โข2018: $136.82B
โข2023: $307.39
โขCAGR: 17.57%
FREE CASH FLOWโ
โข2018: $22.83B
โข2023: $69.50B
โขCAGR: 24.93%
NORMALIZED EPSโ
โข2018: $2.19
โข2023: $5.80
โขCAGR: 21.50%
SHARE BUYBACKSโ
โข2018 Shares Outstanding: 14.07B
โขLTM Shares Outstanding: 12.51B
By reducing its shares outstanding ~11%, $GOOG increased its EPS by ~12.3% (assuming 0 growth)
MARGINSโ
โขLTM Gross Margins: 58.1%
โขLTM Operating Margins: 32.1%
โขLTM Net Income Margins: 27.7%
***NOW TO VALUATION ๐ง
As stated above, investors can expect to receive ~5% MORE in EPS & ~6% LESS in FCF per share
Using Benjamin Grahamโs 2G rule of thumb, $GOOG has to grow earnings at an 11.24% CAGR over the next several years to justify its valuation
Today, analysts anticipate 2025 - 2027 EPS growth over the next few years to be more than the (11.24%) required growth rate:
2024E: $7.98 (37.5% YoY) *FY Dec
2025E: $8.95 (12.2% YoY)
2026E: $10.19 (13.8% YoY)
2027E: $11.78 (15.6% YoY)
$GOOG has an excellent track record of meeting analyst estimates ~2 years out, so letโs assume $GOOG ends 2027 with $11.78 in EPS & see its CAGR potential assuming different multiples
24x P/E: $282.72๐ต โฆ ~14.0% CAGR
23x P/E: $270.94๐ต โฆ ~12.4% CAGR
22x P/E: $259.16๐ต โฆ ~10.7% CAGR
21x P/E: $247.38๐ต โฆ ~9.0% CAGR
20x P/E: $235.60๐ต โฆ ~7.3% CAGR
As you can see, $GOOG appears to have attractive return potential IF we assume >22x earnings (a multiple below its 5-year & 10-year mean)
At >24x earnings, $GOOG has aggressive CAGR potential & itโs not unreasonable for the business to even trade for ~24x (given its growth rate, moat, balance sheet, & exemplary capital allocation)
Those buying $GOOG today at $191๐ต are getting a great business at a more than reasonable price, ensuring a slight margin of safety
Between cloud โ๏ธ , AI ๐ค , quantum computing โ๏ธ, $GOOG has a strong growth runway ahead
I consider $GOOG a strong buy with a substantial margin of safety closer to $179๐ต where I could conservatively expect double-digit CAGR while assuming a 20x multiple
$GOOGL
___
๐๐๐๐๐๐๐๐๐๐โผ๏ธ: ๐๐ก๐ข๐ฌ ๐ข๐ฌ ๐๐๐ ๐๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐. ๐๐๐๐ฒ๐ฅ๐จ๐ง ๐๐๐ฉ๐ข๐ญ๐๐ฅยฎ ๐๐ง๐ ๐ข๐ญ๐ฌ ๐ซ๐๐ฉ๐ซ๐๐ฌ๐๐ง๐ญ๐๐ญ๐ข๐ฏ๐๐ฌ ๐ฆ๐๐ฒ ๐ก๐๐ฏ๐ ๐ฉ๐จ๐ฌ๐ข๐ญ๐ข๐จ๐ง๐ฌ ๐ข๐ง ๐ญ๐ก๐ ๐ฌ๐๐๐ฎ๐ซ๐ข๐ญ๐ข๐๐ฌ ๐๐ข๐ฌ๐๐ฎ๐ฌ๐ฌ๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ.
๐๐ก๐ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐๐ข๐ง๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ ๐ข๐ฌ ๐ข๐ง๐ญ๐๐ง๐๐๐ ๐๐จ๐ซ ๐ข๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง๐๐ฅ ๐ฉ๐ฎ๐ซ๐ฉ๐จ๐ฌ๐๐ฌ ๐จ๐ง๐ฅ๐ฒ ๐๐ง๐ ๐ฌ๐ก๐จ๐ฎ๐ฅ๐ ๐ง๐จ๐ญ ๐๐ ๐๐จ๐ง๐ฌ๐ญ๐ซ๐ฎ๐๐ ๐๐ฌ ๐ข๐ง๐ฏ๐๐ฌ๐ญ๐ฆ๐๐ง๐ญ ๐๐๐ฏ๐ข๐๐ ๐ญ๐จ ๐ฆ๐๐๐ญ ๐ญ๐ก๐ ๐ฌ๐ฉ๐๐๐ข๐๐ข๐ ๐ง๐๐๐๐ฌ ๐จ๐ ๐๐ง๐ฒ ๐ข๐ง๐๐ข๐ฏ๐ข๐๐ฎ๐๐ฅ ๐จ๐ซ ๐ฌ๐ข๐ญ๐ฎ๐๐ญ๐ข๐จ๐ง. ๐๐๐ฌ๐ญ ๐ฉ๐๐ซ๐๐จ๐ซ๐ฆ๐๐ง๐๐ ๐ข๐ฌ ๐ง๐จ ๐ ๐ฎ๐๐ซ๐๐ง๐ญ๐๐ ๐จ๐ ๐๐ฎ๐ญ๐ฎ๐ซ๐ ๐ซ๐๐ฌ๐ฎ๐ฅ๐ญ๐ฌ.
๐๐ง๐๐จ๐ซ๐ฆ๐๐ญ๐ข๐จ๐ง ๐๐จ๐ง๐ญ๐๐ข๐ง๐๐ ๐ข๐ง ๐ญ๐ก๐ข๐ฌ ๐ญ๐ฐ๐๐๐ญ ๐ก๐๐ฌ ๐๐๐๐ง ๐จ๐๐ญ๐๐ข๐ง๐๐ ๐๐ซ๐จ๐ฆ ๐ฌ๐จ๐ฎ๐ซ๏ฟฝ[...]