In Practise

A Primary Research platform for long-term fundamental investors.

[In Practise @_inpractise]

interesting thread

[Pod Risk Manager @sadandlonely_69]

In honor of some “pods blowing up” and me being traveling I will outline some concepts about pod risk management about which I do know a thing or two, as my name might suggest. 

It is a common perception that pods are factor beta neutral and thus largely hedged. That is not quite true. A truer statement is that they are factor beta neutral to the extent they can measure the exposures. 

Most equity risk models are derivatives of the original axioma and barra factor models. There are now many things that go into this but at their core they are a 12 (IIRC) variable linear regression. And mathematically, the unexplained remainder, the error term, is assumed to be the alpha (Upon which the team gets paid). You well recognize most of these factors: size, momentum (short and long), quality, dividend, domicile, sector, etc. 

Now even the dull among you may note that there are in fact many other things that go into a stock. That’s true and that’s one reason why many people at the pods are very rich. There is though a very active and live debate to the highest level of these firms as what you “want” in the other bucket. On the one extreme, scheduling a proprietary drone flyover on the Aurora AV truck up fitting facility to measure parking lot activity vs expected timeline….that feels like pretty good alpha. On the other extreme, going long the Canadian listing rather than the U.S. listing because the Canadian listing diversifies the domicile score so you get a higher portfolio level idio score…..that doesn’t feel like great alpha. But the range is wide: paying GS a billion dollars so your team is the first meeting at the conference? That’s kind of the pod scale advantage. Albeit less so if every other pod is in the room. Going balls long large cap domestic U.S. GLP-1 manufacturers vs non GLP-1? Well that kind of is thematic alpha (depending on the work put in by analyst), but even if you’re of the opinion fuck it, let the kids rip (a reasonable view) you also know at the levered portfolio level that we aren’t measuring that risk completely. This line can really be illustrated with the concept of a “custom factor”. Maybe going long a shit load of SPACs is alpha, maybe it isn’t, but how the FUCK are you gonna build a portfolio level SPAC factor exposure loading in late 2021 when spacs didn’t functionally exist until 2021????

Anyways, interesting & active philosophical debate but there are some downstream derivatives. 

Firstly, as a well known matter, 6 sigma events happen in finance all the time. There’s one every year in some corner of the market. So ex ante we know our models aren’t “great”. 

Second, everyone knows the agent is an unobserved blind spot. It’s impossible to actually “know” why the analyst and PM are doing anything. You like to think they’re doing it on the back of deep fundamental quality work, but the median analyst and PM isn’t good at their job. So that assumption is known to be unreliable. The capital allocation solution to this is momentum chasing at the portfolio level: you allocate more to winning pods and you cut the losers fast. This is a brilliant approach because it means the pod business model is capital light- they identify and scale the good teams without actually having to invest in knowing which pods have “good” process. It’s worth really appreciating that point because it’s one of the true genius aspects of the model, and at its core it’s why the GPs are so fucking rich. (Deep finance readers will note that some of the original papers on factors saw that one of the earliest possible explanations for the decade-long trend nature for factor performance is implicit fund flow pressure from the momentum chasing nature of capital at the LP level)

(Continued in reply)

[In Practise @_inpractise]

Interesting perspective

[Max Anderson @MaxAnderson]

As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty:

This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term

Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries

Google’s response?

Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for

A few examples to illustrate:

For all of its history until recently, Google operated on a 2nd price auction model

I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid

This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much

However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding

It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem

Making thing worse, Google also recently nerfed keyword targeting precision

Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting

This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed

But now, even if you bid on a specific term or phrase using the strictest exact
-match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)”

The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off

So now exact match is broad match, and broad match is just meaningless spam

This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants)

This is how you grow revenue atop declining search volumes

Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day

And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off

Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target

These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow

Google operated a benevolent monopoly for the better part of 25 yrs

Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future

This is now no longer the case

At the alter of AI capex, Google is sacrificing the golden goose

[In Practise @_inpractise]

Ecolab CEO, Q1 2026:

"Our bio processing business, which is the core of our business, well, it grew north of 100% in the first quarter."

And on capacity: "We packed, for now, we need more capacity as well."

$DHR

[In Practise @_inpractise]

great to hear - would love to see Howdens crack the US!

[Todd Wenning @ToddWenning]

A quick thank you to @_inpractise for working with me to source a very niche call on this project. I've worked with them for years and they are fantastic at sourcing expert calls and working with your schedule.

[In Practise @_inpractise]

Former AppLovin account manager on cashback deals tying 90-95% of ad revenue to MAX. $APP

[In Practise @_inpractise]

Weekly Update: October 8

48 expert interviews. Featured:

TransDigm: Airbus capped post-Covid price rises at 12%
Meta vs Amazon: board oversight and cost culture
AppLovin: CloudX and publisher lock-in on MAX

[In Practise @_inpractise]

Former AppLovin account manager on CloudX and the cost of leaving MAX. $APP

[In Practise @_inpractise]

Former Head of Product at Hippo on the renters-to-home cross-sell at $LMND

[In Practise @_inpractise]

@MH_uMG @WillBiddy_ true, although Adair was a tough act to follow. He probably the most experienced salvage guy in the US today. this carries weight when sitting across from insurance claims leaders.

[In Practise @_inpractise]

Yep - it’s always surprises us also how some firms only do meetings with current management and take it at face value without seeking disconfirming evidence from the field…

[Paul Enright @pmje73]

Management meetings/field research is more important than ever. Assume 75% of the work you do at your desk for 8-12 hours a day will be done better by agents running 24/7. Being on the road more should be part of the job description for analysts.

Model build tests as a screen to hire analysts will eventually go away and perhaps be replaced with mock management interviews or mock information synthesis.

Being a nob in shared meetings, sabotaging yourself and those you share with will become fatal.

Every possible input into the numbers inside one of the three financial statements  – product, sales, distribution, input costs, discretionary opex, balance sheet utilization, capital allocation, etc , etc, etc  . . . .  seemingly always fall into one of two buckets. 

1.)      Things within management’s control.

and

2.)      Things outside their control.

When given the opportunity to meet with company executives, it’s almost always more productive, when faced with limited time, to ask specifically about things they control.   There are certain questions only management can answer.  Ask those questions first. Too much time in management meetings is spent asking execs to speculate on short term things outside of their control.

[In Practise @_inpractise]

Manager, Global Quality at Eastman Performance Films on the $XPEL factory timeline

[In Practise @_inpractise]

Former Marketing Director at a CPG e-commerce brand on AI tools beating the $KVYO agent

[In Practise @_inpractise]

Former President of CPI Electron Devices at TransDigm on Siemens holding off on price ($TDG)

[In Practise @_inpractise]

Former VP of Supply Chain Strategy at GE on why Airbus won't reach rate 75 ($AIR)

[In Practise @_inpractise]

Former Executive Director at Wells Fargo on non-bank lenders picking the easier score ($FICO)

[In Practise @_inpractise]

Former VP of Marketing at Huuuge on the Traffic Puzzle handover at $HUG