+ Obsessed with computers from a young age
+ He graduated from MIT with a Computer science degree
+ Worked for Mckinsey in Europe
+ Came back to New York to work for Tiger Global for 3+ yrs until 1999 when Tiger returned capital.
3/ Phillipe w/ lessons from the greatest hedge fund manager started Coatue in 1999 before the dot-com crash.
One of the most impressive things of Coatue is they have escaped all Bubbles WITHOUT damage. This is rare for hf's. Risk mgmt is top for Coatue.
Next is a quote from PL
3i/
"I used to think it ws all stock picking. Truth is, risk mgt is half the battle. Portfolio mgt is like poker, yu don’t always get perfect cards and yu need to manage your stack. My risk mgt is broken down between rules I never break and principles that reinforce good habits"
3ii/ I read they are extremely good at on the short-side of their fund. They want to know everything about a company (complete bull case) and deep-dive before shorting.
The ability to short effectively has induced their fund returns.
4/ Phillipe started his fund with some capital from his mentor Julian and others.
He started with only $45M in 1999.
By 2003, grew it to over $500M
By 2009: $2.2B!
Today, their combined public (25B) + private AUM is over $55B+.
That's almost more than more 100X; How?
5/ Early in 1999's - 2004:
This period can be remembered as the great boom in Asia as these economies opened up.
Coatue bet heavily into the digitization of Asia and was highly successful by being one of the earliest investors into Tencent at around $7, and more Asia names..
6/ Into Post 2007:
Phillipe heavily believed in digitization and the mobile era.
Coatue made a heavy bet on Apple around the launch of the iPhone. $AAPL singlehandedly led the firms biggest performance even thru the turbulent 07' crisis including making other smart investments
7/ Post 2011:
As a result of the rise of smartphones, Coatue made heavy bets on the rise of social media and other trends that would result from individuals having phones. They invested into $SNAP at a $1.5B valuation.
Also early into Lyft, Grab in 2015, Afterpay, Doordash, etc
8/ Today, 2020/21:
What are they doing to differentiate themselves?
How have they been so successful?
They're highly secretive, but the next half of this thread focuses on those key elements:
a) Data Science
b) Rigorous Analysis
c) Index into top deals
Let's break-it-down:
9/ Coatue uses Machine Learning/Data Science on Alternative datasets to inform ALL investment decisions:
Nothing is new with ML but Coatue is known for inventing & holds the most advanced data science hedge fund.
Today, they have 50+ Data Scientists in a fund with <170 staffs.
10/ In an interview, their Data Science chief said their Investment team is structured in this format:
a) Data Engineering & Infrastructure Team,
b) Research & Statistical Modelling Team
c) Insights Team: Scientists + Finance + Analysts work.
Unique from a traditional L/S hf.
11/ Go on LinkedIn to see Coatue staffs, you'll find almost more Data scientists than Analysts. Their Ex-Head of Data Science said they hire Finance Analyst with backgrounds in Coding/ML/Tech.
They believe combining traditional fundamentals w modeling differentiates Coatue.
12/ What example of Alternative Data do they use?:
+ Satellite data
+ Mobility data trends
+ Invoices & SKU scan
+ Credit Card datasets
+ App Downloads
+ Advanced social media analysis
+ Analysis of large bulk amt of expert calls networks, and expert analysis.
Continued:
13/ A-data (PT 2)
+ Archive library
+ FB Ad Library
+ Industry Reports-Gartner
+ Job Postings
+ Glassdoor/NPS
+ UserID Tracking
+ Google Trends
+ SimilarWeb|Alexa Web
It goes beyond this list. Just an example and it's a systemic process of using ML to analyze all the following.
14/ Second key trait of Coatue is rigorous & thorough analysis of a company & its industry.
No one knows if they use consulting firms like Bain (similar to Tiger), but it is said they purchase an excessive amt of alternative data. They want to knw a company better than insiders.
14/ Third trait I learned is Coatue is known for digging deeper into a TAM for a Co.
They measure and investigate TAM Opportunities/secular trends before using alternative data/machine learning to find who could be the potential winner of that market.
15/ Phillipe Laffont on TAM:
"What has worked for me is focusing on TAM (market size), earnings, growth and the corresponding P/E multiple 5-7 years out. That means I’m a very mediocre macro and cyclical investor though I need to be reminded of that more than I care to admit."
16/ Fourth trait, as mentioned is that they are bullish on Asia and Chinese tech growth over the next upcoming years. Julian Roberston of Tiger popularized this strategy.
They've been early investors in Meituan, but made a very recent big investment into $BZ Kanzhun.
17/ Current Bets:
The only reason I paid attention to their portfolio is that I love Investors with concentrated portfolios as it shows conviction
Coatue invests in <80 stocks. This is *fairly* tight for a $50B AUM compared to Blackrock or T-Rowe that have Large bets everywhere
18/ Coatue Portfolio:
They were recent big winners into $MRNA, $TSLA early into the pandemic.
Current key themes; Ride-sharing, Food delivery, AI and Cloud Tech themes.
These are their largest positions today: courtesy of @theTIKR
$SNOW $DASH $FB $SQ $SE
19/ I spent some time analyzing the transactions btw Tiger and Coatue (Since they live in the same building in NYC!)
I found similarities of buys through Q2/Q3:
i) $DASH (likely allocation from privates)
ii) $SNOW (Likely behind the recent before Oct)
iii) $PATH in Q2
iv) $SE
20/ Coatue Portfolio largest buys over the past year:
I dug deeper to do some analysis to see the key similarities. They made an unusually big position in $MQ.
Other Notable New Positions: $CFLT, $JD, $GTLB, $TOST, $AFRM, $ZM, $RBLX
21/ I'll wrap up my thread with my best quotes and impressions from reading Phillipe Laffont.
It is said that Phillipe is a brilliant, rigorous, under-the-radar, and tough boss with VERY standards for his analysts.
a/ "To be a good public investor, You don't need to be a genius or own a crystal ball, but you do need to make a few big calls"
23/
b/ "To be a growth investor, you need a growth mindset: stay curious and flexible. I try to invest 5-10pct in areas i’m not comfortable in. The corollary is less obvious. It’s hard to sell winners but a portfolio is like a sports team and you need to refresh the superstars."
24/
c/ "Imagination is the skill. Uncertainty is the opportunity. Data is important but so is the story. Patience is way underrated."
25/
d/ "Disclaimer: Growth investing is not momentum investing. The best growth investors find new trends and extraordinary founders but still think of business models and valuation. And they are disciplined risk managers."
26/
e/Last thing from my research is that PF believes evryone can be successful in growth investing. See his YT videos. PF believes it takes hard work, focus, process, data and extending your time horizon.
Using ML on alternative data is nt new, but Coatue is definitely utilizing it in a unique way and it shows in their returns. The firm appears to be like a tech start-up. This is definitely a fund to watch!
30/ END:
Thanks for following along. I enjoyed going down this unexpected rabbit hole today. If this was helpful, feel free to share!
As part of this, I also dug into Chase Coleman, another fascinating personality. If people are interested, I could do a future thread.
Thanks!
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Some quick thoughts 1/
2/ My priority is to focus on companies that can sustain 30-40% growth rates for a 3-5yr.
The market is looking for companies that have growth PLUS (optionality, product-lock-in, switching costs, mission-critical) to deduce the durability of a 3-yr CAGR to decide its multiple.
3/ Other things is looking for 30%+ CAGR Rev growth combined with lock-in, high land & expand DBNRR?
Alternatively, for non-SaaS companies. I'm looking for companies that are still relatively early in capturing their TAM and are showing qualities that they are market leaders?
Let's begin w/ positives:
• Current Q3, 70% YoY growth
• 50k Customer growth was 132% (highest since 2020)
• 5k & 50k Customers are sticky, spending more: DBNRR of 130% and 145% respectively to last Qtr
Where were the negatives? 1/6
2/ Reason the stock is down:
• Revenue of 70% YoY, slight deceleration
• Q4 Guide: 54% YoY, 5% sequential slowdown from 12% QoQ from Q1-Q2
• FY Guide: 64%, light
• FCF: -29% & high S&M/Operating exps remaining the same
+ This is unacceptable when yu trade for EV/S of 60+ :(
3/ $ASAN: It turns out this indicator in my thread came true.
Enterprise customers were more likely to Decrease their spending on $ASAN over the next couple of months which is why $ASAN likely had a slow guide.
This is a phenomenal breakdown of today's Enterprise AI/ML stack (S/O: @BatteryVentures).
I'm constructive on the tailwinds within this space and I'm long: $CFLT, $CRWD, $AMPL, $PLTR.
The report below is a great read for any AI & Enterprise SaaS Enthusiast.
Thread Summary: 1/
2/ Today's Org has a fragmented data and AI environment. Here are the different job roles required to perform certain data tasks.
For example, 1) the ETL process is going to undergo some innovation. 2) More people will become data citizens IMO.
The visual below shows it.
3/ The Data Value Chain:
The picture below explains -
i) the lifecycle of machine learning in an organization,
ii) the different applications required, and
iii) how data flows through the modern organization.
2/
- Q3: 40% YoY would be Net $30M, 35% ideal
- Q4 Rev: $405M (>27% YoY) is a minimum.
- FY 2021: $1.6B which would be roughly 40% YoY would be great.
- Sadly, I expect high SBC Costs
- Gov't contracts should boost FY 21 based on recent contracts (Army, IDIQ)
Metrics below
3/ Enterprise Clients is a BIG PART of my thesis:
Acceleration in the commercial would be HUGE. In Q2, they added 20 net new clients (booked $925M of contracts (175% YoY): Could we get around 190+ in Q3?
I'll watch Billings, RPO Growth, and Total Contract Value, ideally $4.0B+>
Confluent: The Central Nervous System that is empowering Data in Motion for the Leading Corporations.
Why Confluent could become as dominant as Snowflake as a leading Open-Source Data Infrastructure.
This is my long-awaited $CFLT Investment Thesis:
1/Thread Overview:
1) First Principles Analogy for Kafta (the foundations of Apache Kafta is needed to know CFLT) 2) $CFLT Overview and how it fits Kafta 3) Investment Thesis 4) The AI & Data Analytics Opportunity
Let's GO!
2/First, we need to know Apache Kafta?
An analogy: Imagine the task of going shopping, arriving home and you need to distribute those bought items across a BIG house FAST. Hassle? What if I gave you a robot to perform that task? Yay!