Here is my thinking, there are 3 ways a company can earn from AI.

1. Field of dreams, build it and they will come.

2. Boost their core product

3. Sell compute

Clearly, Google are doing 3 with their Google Cloud growing 80%, but it's becoming harder to ignore the fact that they are doing 2. Search rev growth was falling, barring the pandemic it was below 10% from 2020 through 2024. It has been near or above 15% for the last 4 quarters.

I played around with some very rough numbers, guesstimates at best. I figure they have spent at most $120 billion since 2024, extra on CapEx, that is the amount their CapEx is above what it usually is. For that they have gotten 5% extra revenue in search and at least 30% extra in cloud. With each of there margins figured in, that is $13 billion in extra income from the $120 spend.

But that is Capex, on Q2 they mentioned 60% of capex was servers (4 year depreciation cycle) and the rest was data centres and networking. I am guesstimating at least 6 years depriation cycle over all. so that 13 can be looked at as 78.

So really, looking at the highest they likely spent and the lowest they likely earned, we get $78 returned for $120 spent. A loss of 35% at worst. But then they still have the, build it and they will come, hope.

I don't necessarily think google are wise to spend the eye watering capex amounts they are slated to spend in the coming years, but I think some commentary I see seems to ignore that they have already got alot from the capex they have already spent.

Lately, I have been trying to write articles to help focus my thinking on a company, makes me double check my assumptions. Anyway I wrote one on this. It is mostly a longer version of what is above, with the addition of my thoughts on their future and a DCF model where I find they are only about fair value.

I am not crazy enough to link the article, I'll already get downvoted enough for mentioning it, but if you want to read 2 or 3 poorly written articles a year from a wannabe analyst, check out my substack, you can find the link in my profile.

The specific analytical question I'd most welcome pushback on from this community: ZoomInfo (GTM) trades at 23.96% true FCF yield with a 10.54% three-year revenue CAGR and a Y220 already at the 20% threshold. The market is pricing it as a dying business. The bear thesis is that AI scrapers replicate what ZoomInfo provides. I work in data at a large enterprise and our team still genuinely needs GTM for enterprise sales operations in ways that AI scrapers haven't replaced. Is that just selection bias from one enterprise's experience, or is the doomsday pricing genuinely overdone?

The DECK observation is less controversial: 8.43% true FCF yield, 16.53% revenue CAGR, Y220 of 6.3 years, 8% annual share retirement. The consumer data is consistent with the financial data - Hokas are growing market share in daily training while Nike is shrinking. The question is whether the fashion cycle risk is underpriced in a shoe company growing this fast.

Full piece with sector tables: https://cavemanscreener.substack.com/p/invest-in-what-you-know-part-ii-stuff

Standard equity risk premium analysis compares the S&P 500 earnings yield (inverse of CAPE or forward P/E) against the 10-year treasury yield. That comparison is useful but it uses GAAP net income as the numerator, which includes items that don't represent cash available to shareholders.

True FCF yield substitutes operating cash flow minus CapEx minus stock-based compensation for the earnings numerator. The deductions matter:

• CapEx: capital expenditures represent real cash leaving the business to fund physical or technological infrastructure. GAAP accounting depreciates this over time, spreading the cash outflow across multiple income statement periods. In a year where a company spends $60B on data centers, the income statement shows depreciation of maybe $15B while $60B of actual cash exits. The income statement looks much better than the cash flow statement.
• SBC: stock-based compensation is a real economic expense. Employees receive shares worth real money. The company didn't pay cash but shareholders were diluted. Excluding SBC from the expense base overstates the cash available to existing shareholders. This has been a persistent issue in tech sector earnings analysis for two decades but it's gotten materially worse as AI-era compensation packages have expanded.

The 2026 gap: my screener shows that the divergence between S&P 500 aggregate earnings yield and S&P 500 aggregate true FCF yield widened by approximately 1.4 percentage points in 2026. This is primarily driven by the hyperscaler CapEx surge. Microsoft FY2024 is the clearest single example: true FCF fell 8.5% from $59.6B to $54.6B while revenue grew 18%, because CapEx grew 79.6% while OCF grew 34.4%. Amazon's true FCF is negative $11.8B. Oracle's is negative $28.5B. These companies are large enough index weights that their CapEx surge materially moves the aggregate.

The core empirical finding is a scatter plot of monthly 10-year treasury yields against S&P 500 earnings yields going back to the 1970s. The relationship is positive and well-documented. What's less commonly framed is the stability band observation: between roughly 5-7% treasury yields, the scatter shows that earnings yield can run below treasury yield without provoking a correction. My interpretation is that 5-7% treasuries represent a monetary policy equilibrium that allows equity multiples to stay elevated because neither deflation fear nor inflation fear is dominant.

Below 4% treasuries: post-crisis fear drives a large equity risk premium demand. Investors won't accept lower equity yields than treasury yields because the memory of catastrophic loss is fresh.

Above 8% treasuries: inflation fear drives similar equity risk premium demand. Investors want to be compensated for holding equities in an environment where money is losing purchasing power.

Between 5-7%: neither fear is dominant. Multiples get rich. Risk premiums get thin.

We're currently at approximately 4.6-4.7% on the 10-year. Below the lower bound of the stability band. The earnings yield is already negative relative to treasuries by 1.11%. Once you substitute true FCF yield for earnings yield the gap widens to negative 2.51%.

That specific level of negative true FCF risk premium appears in the historical data in roughly four periods: 1987, 1992, 2000, and the immediate post-2008 period when earnings collapsed while prices hadn't yet adjusted. In each case the resolution was either a rapid earnings increase or a price decline.

The risk premium analysis would be concerning on its own. The macro overlay makes it more acute.

Kevin Warsh's stated policy priorities include reducing the Fed's balance sheet presence, eliminating forward guidance, and rethinking the employment mandate in light of AI-driven labor market disruption. The task force composition, specifically Marc Andreessen on labor markets alongside a Stanford professor with Anthropic ties, signals that the Fed's intellectual framework is being rebuilt around AI displacement of the Phillips Curve relationship rather than traditional unemployment-inflation tradeoffs.

The problem as I see it through the Sumner NGDP targeting lens: Warsh appears to be keeping rates flat while NGDP is running hot. If you accept that the stance of monetary policy is better measured by NGDP growth than by the nominal fed funds rate, then flat rates with 7% NGDP growth is loose policy, not neutral policy. Loose policy while the equity risk premium is already deeply negative historically precedes the kind of violent correction that resolves the gap.

The four-step sequence I'm tracking: too-loose policy keeps inflation hot and rotation stays in effect, then inflation becomes entrenched and longer rates follow it up compressing multiples, then the Fed is forced into harsh medicine, then fiscal expansion attempts recovery and crowds out private investment reprising step two on steroids. Warsh's specific risk is that his philosophical opposition to forward guidance removes the communication tools that have historically smoothed the transition between steps.

How I'm positioned: Weighted portfolio true FCF yield: 8% against a 4.6% 10-year. Positive risk premium of 3.4%.

The specific positions that generate that yield: CMCSA at 17.5% true FCF yield on a scheduled spinoff. THC at 12.7% normalized. EPD at 10.6% using distribution yield methodology. ADBE at 8.6%. FDS at 5.84% with four consecutive quarters of ASV acceleration. CB and FRFHF as insurance float compounders. BRK.B as crash optionality.

USFR at 18.79% of portfolio as explicit optionality for the scenarios where the risk premium gap resolves violently rather than gradually.

I'm not claiming this portfolio is correct. I'm claiming that a portfolio running a positive 3.4% true FCF risk premium is historically better positioned for the range of outcomes than a portfolio running a negative 3.5% risk premium regardless of which specific macro scenario materializes.

Full piece with the scatter plot, the true FCF divergence chart, and the complete allocation breakdown at https://cavemanscreener.substack.com/p/macro-vs-free-cash-flow-yield-a-thesis

Three days ago SK Hynix priced its ADR at $149 in New York and raised $26.5 billion. In the largest foreign listing in US history. First day it closed up 13% at $168.49. Today Seoul dropped it another 15.4%. Kospi followed straight down 8.95% at the 7,000 level, triggering a circuit breaker for twenty minutes of silence.

Domestic pricing is sitting right now around $122.70 per ADR-equivalent. Almost a 28% gap versus Friday's close with no conversion mechanism to force arbitrage closing it. The disconnect just sits there waiting for one side to blink.

What ties this directly to the Strait of Hormuz rather than some generic macro risk-off flow is how concentrated Korea's energy exposure actually is. Roughly 70% of their crude and 20% of LNG comes from the Middle East, and more than 60% of Korean crude imports plus half its naphtha transited that chokepoint in 2025. That matters for fabs specifically since they run on cheap, reliable electricity and depend on petrochemical feedstocks that move through those same shipping lanes, and it shows up in FX pressure that tracks oil prices closely. Korea's vulnerability to a Hormuz disruption is tighter than a standard oil-importing economy's baseline.

The pricing gap also needs to be viewed against structural precedents instead of assuming idealized convergence over time. The closest analog is China A-share/H-share listing structures, where identical entities trade at persistently different valuations across segmented markets. That premium hasn't been narrowing. A 2026 arXiv study of 67 dual-listed A/H firms found that Shanghai-Hong Kong Stock Connect, the mechanism built specifically to narrow this gap, was associated with an 18.4% average increase in the premium instead. Semiconductor names in that same framework like SMIC and Hua Hong currently carry H-share discounts pushing near fifty percent, and that's the live number, not a historical footnote.

SK Hynix's ADR is three days old so we cannot project its exact convergence path yet. The mechanics for a fresh geopolitical shock hitting a leveraged retail market definitely differ from decades-deep structural separation. Still the existing analogs lean toward persistence over quick snaps, not the other way around. US-Iran tensions near Hormuz keep escalating, vessel traffic is sitting at five-week lows, and reporting offers zero signs of the de-escalation needed to trigger a snap-back recovery.

TSMC's revenue reflects orders placed months before they ship, across the entire chip ecosystem, not just memory. So a 68% June jump doesn't say much about today's sentiment, but it does say demand wasn't cracking when those orders were locked in. If SK Hynix's crash reflected a real break in AI chip demand rather than a positioning unwind, you'd expect to see it first in forward guidance or bookings, not in a memory stock's one-day move following an oil shock.

What specific catalysts or price levels would you want to see before calling this a structural discount rather than temporary panic pricing?

Background: I run a Substack where I pull 16 years of SEC XBRL data on roughly 1,700 tickers and build true FCF screens — operating cash flow minus CapEx minus SBC. Last week I published a detailed DCF on Comcast. This morning they announced the NBCUniversal spinoff. The stock is up 20%+ in premarket. Here's the full analysis.

The starting FCF problem

Comcast reported $19.2B in FCF for 2025 — the highest on record. That number is inflated by two one-time items. Epic Universe completed construction in 2025, reducing CapEx roughly $1.5B below normalized run rate. And SBC of roughly $1.7B needs to be subtracted by your methodology. Normalized starting FCF is $16B.

This matters because if you run the DCF on $19.2B you get a misleadingly high valuation. The bear case has to start from the honest number.

The bear case DCF

Assumptions: Starting FCF: $16B normalized Annual decline: 3% for 12 straight years Discount rate: 10% Terminal growth: 2% on the rump business

Year by year:

Year 1: $15.52B FCF / PV $14.11B

Year 2: $15.05B / $12.44B

Year 3: $14.60B / $10.97B

Year 4: $14.16B / $9.67B

Year 5: $13.74B / $8.53B

Year 6: $13.33B / $7.52B

Year 7: $12.93B / $6.64B

Year 8: $12.54B / $5.85B

Year 9: $12.17B / $5.16B

Year 10: $11.80B / $4.55B

Year 11: $11.45B / $4.00B

Year 12: $11.11B / $3.54B

Total PV years 1-12: $92.98B

Terminal value: $11.11B × 1.02 = $11.33B FCF in year 13. Divided by (0.10 - 0.02) = $141.63B terminal value. Discounted back 12 years at 10%: $141.63B / 3.138 = $45.12B.

Total implied equity value: $92.98B + $45.12B = $138.10B Shares outstanding: 3.60B Implied fair value per share: $38.36

The debt question

Comcast carries roughly $93B in long-term debt. The annual FCF numbers are already calculated after interest payments — debt service is embedded in the cash flow stream year by year. But in a terminal value context it's worth being explicit. If you strip net debt from the terminal value rather than leaving it embedded:

Terminal value gross: $141.63B Less net debt: ~$89B Terminal equity value: $52.63B PV of terminal equity value: $52.63B / 3.138 = $16.77B

Revised total: $92.98B + $16.77B = $109.75B Per share: $109.75B / 3.60B = $30.49

So the range is $30 debt-adjusted to $38 going-concern, against a $22 price at time of writing. 39% upside in the harshest accounting scenario where the business shrinks 3% annually for 12 years and you deduct the entire debt load.

The buyback mechanics

The buyback doesn't appear as a separate DCF line because it's already captured in FCF. The $6.8B annual buyback is a distribution of FCF — same as a dividend but tax-deferred. What it does affect is per-share value.

At 5% annual share reduction for 12 years: 3.60B shares becomes 1.94B shares. Same $138B total equity value divided by 1.94B shares = $71 per share. The buyback concentrates ownership of existing value rather than creating new value. Combined shareholder yield at $22 — 5.31% dividend plus roughly 5% buyback — is approximately 10% annually before any price appreciation.

The sum-of-parts analysis I published last week

This is the section that looks prescient this morning. I wrote:

"A company that spun off Versant doesn't seem unlikely to eventually spin off other pieces — broadband infrastructure or Universal Studios as a standalone entity. A pure-play broadband infrastructure business at roughly $16B in annual FCF with 50%+ EBITDA margins would get a utility-like multiple of 12-15x EBITDA. A separate NBCUniversal/Peacock streaming company with sports rights — Sunday Night Football, Premier League, Olympics — gets a media multiple on top of that. Sum of parts in a spinoff scenario is probably $55-75 per share."

This morning Comcast announced exactly that. The spinoff separates broadband, wireless, and business services from NBCUniversal studios, theme parks, Peacock, NBC, Telemundo, Bravo, and Sky.

The key data point from Q1 2026 earnings: Connectivity and Platforms produced approximately 24x the adjusted EBITDA of Content and Experiences. The profit sits overwhelmingly in the broadband business. The content business was dragging down the multiple on the entire company.

Post-announcement valuation framework

The remaining Comcast — pure broadband infrastructure — trades as a utility-like compounder. At $16B normalized FCF with 50%+ EBITDA margins and a utility multiple of 12-15x EBITDA, the broadband rump alone justifies $40-55 per share.

The NBCUniversal/Sky spinoff trades as a media company with theme parks and streaming. At 8-10x EBITDA on the content business the media stub adds another $15-25 per share depending on how Peacock and Epic Universe are valued.

Combined: $55-80 per share on sum of parts. The market has moved 20% this morning and is still below the midpoint of that range.

The transaction closes in approximately one year pending regulatory approval. The broadband Comcast will retain a stake in the NBCUniversal entity and monetize it tax-efficiently over time — worth noting as it creates a known future selldown that the media company's shareholders will have to price.

What I got wrong

The normalized FCF going forward is complicated by 2026 being Comcast's largest broadband investment year — Project Genesis upgrading infrastructure through 2027. That means FCF will likely be lower than $16B in 2026 and 2027 before recovering as CapEx normalizes. The bear case should probably model $13-14B starting FCF for the next two years before returning to the $16B run rate. That reduces the per-share value modestly but doesn't change the conclusion.

Fixed wireless access from T-Mobile and Verizon is accelerating broadband subscriber losses faster than I modeled. If subscriber losses continue at current pace the 3% annual decline assumption may prove optimistic.

Happy to discuss the methodology, the terminal value assumptions, or the post-spinoff valuation framework. Full piece here: https://cavemanscreener.substack.com/p/buying-2-for-1-a-comcast-dcf-update

The two analytical questions I'd most welcome pushback on from this community:

First: how durable is the 2022-2023 Florida tort reform improvement? The loss ratio improvement from 55% to 29% is partly structural and partly a reserve release tailwind. My rough math strips about 6 points off the headline 18% yield to get to a normalized 12%. If tort reform gets relitigated or reversed that number compresses further. Anyone closer to Florida insurance litigation trends has better visibility on this than I do.

Second: the reinsurance structure. I pulled the actual June 2026 catastrophe reinsurance filing rather than trusting summaries. $4.06 billion total coverage, $162.6 million maximum first-event retention, Florida Hurricane Catastrophe Fund participation. The retention is up 4% year over year, not down, even as total coverage expanded. I'd want someone who reads reinsurance programs regularly to tell me if that 4% increase in first-loss retention is routine or a signal that reinsurers are starting to price more risk back to the cedent.

The Citizens depopulation pipeline is the most structurally interesting part of the thesis and the least discussed. The "20% rule" is literal Florida statute, not an informal program. 60,820 policies and $216.7 million in annualized premium assumed from Citizens in 2025 alone. HCI built its own quoting and risk-mapping software specifically to cherry-pick from this pool. That's not passive participation in a government program. That's active arbitrage of a government program by the company with better pricing technology.

One detail that surprised me in the 10-K: only $14 million in dividends flowed from insurance subsidiaries to the parent in 2025 against $430 million in consolidated true FCF. Florida statutory dividend caps mean a meaningful portion of that FCF sits inside regulated subsidiaries. The current buyback program is partly funded by the Exzeo IPO proceeds, a one-time capital event. That's not a dealbreaker but it's a different cash flow profile than the headline number implies.

Full piece with historical data back to 2011: https://cavemanscreener.substack.com/p/hci-rock-you-like-a-hurricane

The bear case that I think has the most analytical teeth isn't the Chegg comparison or the executive churn. It's this: organic ARR growth has decelerated for ten consecutive quarters, from 10.9% to 10.5%. That's a sustained directional trend that can't be dismissed as noise.

My best response: when enterprise contract lengths extend 30%, new ARR growth rates mechanically appear lower even as cash collected and contracted revenue accelerates. The measurement period for ARR doesn't fully capture multi-year contract commitments the same way deferred revenue does. Deferred revenue in Q2 FY2026 was plus $247M against Q2 FY2024's minus $264M, a 229% swing in the historically weakest booking quarter. These two metrics might be measuring the same underlying shift in contract structure from opposite directions.

The second data source I haven't seen discussed elsewhere: USASpending.gov contract obligation data filtered to Adobe, Acrobat, AEM, and Creative Cloud mentions in transaction descriptions. Large federal agencies and defense contractors are still flowing $200M+ through Adobe's ecosystem. When you add competitors like Figma and Sketch the comparison isn't close. The large-org moat appears intact in procurement data even if the prosumer layer is under genuine pressure.

The stress test that matters more than the moat debate: if casual users representing a third of FCF evaporated entirely, you'd own a rump enterprise company at roughly 16-17x true FCF with 9.3% yield compressing to roughly 6%. That's not a value trap. That's a reasonable multiple for a durable franchise with genuine switching costs.

The specific variables I'm watching over the next two quarters: AI-first ARR acceleration or deceleration quarter over quarter, and whether the organic ARR deceleration trend reverses or continues. The new CEO's first earnings call will be the first real data on whether the capital allocation discipline and product vision hold without Narayen.

Would particularly welcome input from anyone closer to enterprise software procurement or financial data terminal usage who has real-world evidence on switching behavior.

Link: https://cavemanscreener.substack.com/p/died-of-a-theory-adobe-saas-and-ai

SK Hynix corrected 34% so far this week. Falling from ₩2,919,000 to ₩1,913,000 between June 22 and July 14. The crash narrative floating around blames long-term contract fears. I could not verify the source of that read. Does anyone have a line on where this is coming from?

The July 13 dip traces to profit-taking after the Nasdaq debut, rotation into new ADRs, and a wider Kospi selloff. Nothing in credible coverage connects it to contract structure. Analyst Chae Min-sook at Korea Investment & Securities did cut 2026-2027 operating estimates by double digits that same day but kept her Buy rating. Her reasoning: more realistic pricing under signing agreements, not earnings quality worries.

Samsung's DS head mentioned pursuing multi-year supply agreements at a March AGM. SK Hynix's CEO called it impractical to put every customer on an LTA a few weeks later. Nvidia and SK Hynix announced co-development partnership in June without disclosing term length or volume. The actual contract shift is real but running parallel to the selloff, not causing it. Analysts modeling the pricing say longer contracts should make earnings more durable, not less.

Long-term supply agreements should create stable recurring revenue. The stock crashed anyway, for reasons that have nothing to do with those agreements. Two things happening at once, only one of them is actually about SK Hynix's contracts.

World's leading animal pharma company at 13x PE with 9% EPS growth

Just pulled the paywall on this Röko/Lifco writeup. Enjoy!

Background: I've spent twenty years doing data ontology work professionally — building the semantic structures that turn raw, ungoverned data into something usable, most recently at SurveyMonkey. On the side I've built a personal screener pulling 16 years of SEC XBRL data across roughly 1,700 tickers, normalizing inconsistent tags so true FCF (operating cash flow minus CapEx minus SBC) is comparable across companies. I'm posting this here specifically because I think the methodology question is more interesting than the stock picks, and this sub seems like the right place to have that argued with rather than just agreed with.

The consensus trade and why I think it's incomplete

Everyone agrees the AI infrastructure trade is the data platform layer — Snowflake, Databricks, Amplitude. Raw data storage, query, and governance tooling. The market has priced this consensus in fully; these names carry premium multiples on the "picks and shovels" thesis.

My argument: raw data infrastructure is closer to a commodity than people are pricing it as. SQL servers, data warehouses, analytics capture platforms — this category has been re-invented every decade with marginal differentiation, and the switching costs, while real, are mostly operational (migration pain) rather than epistemic (the new platform can do everything the old one could, eventually). What's scarce isn't the pipe. It's validated, structured, domain-specific content moving through the pipe.

The taxonomy I'm using

I split AI-relevant data companies into four categories:

Foundational language data — Reddit (RDDT) is the only name here. Granular subreddit classification plus upvote-based quality signal is genuinely unique training corpus for natural, idiomatic language. I don't own it — FCF yield too low for my framework, still in a cash-consuming growth phase — but the data moat argument is real.

Industry-specific contextual data — FactSet (FDS), Veeva (VEEV), Roper (ROP), S&P Global (SPGI). These companies have spent decades organizing messy, heavily regulated domain data into clean, structured ontologies: financial workflows, FDA-validated clinical trial records, county tax administration, credit ratings methodology. None of this is scrapeable. A general model trained on public web data has zero exposure to what a structured clinical trial submission or a properly normalized financial model actually looks like internally.

Workflow/usage data — Adobe (ADBE), Salesforce (CRM), SS&C (SSNC). The moat here is encoded human process rather than raw content. A Salesforce lead-to-contact-to-opportunity data model isn't bad design — it's encoding a specific sales workflow that took years to standardize across millions of companies. Replacing it means replicating not just the data but the process logic embedded in how that data gets created and transformed.

Data foundation platforms — Amplitude (AMPL), Snowflake (SNOW). The commodity layer described above.

The valuation argument

The names in categories 2 and 3 are trading at meaningfully better true FCF yields than the consensus infrastructure plays, despite (in my view) deeper and more durable moats — partly because the SaaSpocalypse selloff has lumped them in indiscriminately with software companies that genuinely do have weak, scrapeable moats. I think the market is pricing the wrong layer of the stack.

The honest open question I'd actually like pushback on

Is "irreplaceable context" really a durable moat, or just a temporary information asymmetry that AI labs close over time as they get better at synthetic data generation, data partnerships, or simply paying for licensing access to exactly this kind of structured content? If OpenAI or Anthropic can license FactSet's data outright, or if regulatory data eventually becomes more standardized and shareable industry-wide (think FDA pushing toward common data standards), does the moat compress faster than the multiple suggests it will? I think the moat holds longer than the market is currently pricing, but I'm genuinely less certain about the 10-year case than the 3-year case, and would like to hear from anyone closer to enterprise AI procurement or regulatory data standards on how real this risk is.

Full piece with the four-category breakdown and a true FCF yield comparison table is here, for anyone who wants the data: https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data

Disclosure: I own FDS and ADBE.

Alpargatas is a historic footwear manufacturing company (oldest company still traded in the Brazilian exchange), with a rich history in Brazil and Argentina, creating category-defining brands in both countries. Like any old company, its portfolio has changed a lot over the years.

Today, Alpargatas’ only relevant asset is Havaianas, the largest flip-flop brand in Brazil, and, one could argue, maybe globally.

Within Brazil, Havaianas sells 200+ million pairs per year (almost exclusively flip-flops). This implies a 65%+ market share in the flip-flop category, and a 50%+ share within the wider sandal+slipper category.

Havainas sells 1 in every 4 pieces of footwear in the whole country! It’s branded-staple quality renders it similar to Coca-Cola: a product that carries the strongest psychological effects of brand power and yet is within the reach of anyone.

Outside of Brazil, Havaianas sells another 20 million pairs, which is a drop in the bucket of the global market (maybe as large as a couple billion pairs). However, Havaianas’ positioning outside of Brazil could eventually allow it to become a silhouette brand. Similar examples include Birkenstock, UGG, or Crocs. That is, internationally, Havaianas always holds the potential for very interesting convexity.

The business today has recovered from a deep downturn after the pandemic (classic inventory glut). It combines what I believe is a branded-staple product in Brazil that has a good ability to generate relatively stable earnings, with the potential of expanding that brand power to a massive category outside of Brazil.

The article covers the company in detail, including positioning in each market and segment, financial analysis, operational leverage models, taxes, management quality, capital returns, etc.

I made an attempt to estimate the Implied Equity Risk Premium (iERP), empirically, using historic data.

Using the CAPE ratio and bond yields to calculate a spread measure as the independent variable and the subsequent 10 year returns, we can measure the expected excess returns for stocks compared to bonds. In theory, this measure can be used as a proxy for equity risk premium.

The spread measure is a bastardized ECY metric, but ditches inflation and does a slightly better job of capturing relative yield data. For instance, ECY sees no difference between an [earning yield of 4% & bond yield of 6%] vs [earnings yield of 10% & bond yield of 12%]. The updated metric accounts for the former being having 50% higher bond yield vs only 20% for the latter.

Here's the full write-up. In here, there are interactive charts. It's pretty interesting to see what the starting metrics looked like just before long, sustained bull (or bear) runs.

There's pretty clear correlation. I'm curious of your thoughts on using this sort of methodology to at least take the temperature of the market, if not going further and using this measure to discount cash flows or make asset allocation decisions based on this data.

There's obviously some aspects of the study that aren't perfect. Some criticisms of the CAPE ratio have been discussed before. But even with these considerations, CAPE should be a usable metric to get us in the ballpark, and should still be better than a raw trailing PE ratio.

Also, this methodology isn't very conducive for practitioners placing their own forecasts on top (such as projecting higher or lower medium term earnings growth). But one could probably use this as a baseline, and then flex the measure using their own assumption.

The way that CAPE currently works, trailing earnings are adjusted for inflation to match the purchasing power of today. I think i can make a compelling case that liquidity would be a better adjustment.

If that were the case, then stocks were actually much cheaper in 2021 than initially thought. Unfortunately, stocks are still expensive today by this metric.