Ran a true FCF yield screen across the entire AI value chain: hyperscalers, chip designers, TSMC, memory (Micron), ASML, semi equipment (Lam/KLA), materials suppliers (MKSI/ENTG/UCTT), and a control group of "beaten-down SaaS" names the market has priced as AI casualties (ADBE, CRM, FDS, VEEV, NOW).

Methodology: true FCF = (OCF - CapEx - SBC) / market cap. Flagging a data quality issue upfront since I think it matters for credibility: TSMC's true FCF yield in my dataset is overstated because of a known tag mapping issue with their 20-F IFRS filing in my XBRL pipeline. Actual CapEx runs $30-35B/year; the zero showing in my raw pull is a pipeline bug, not a real number.

Findings: most of the hardware/hyperscaler chain trades below the risk-free rate on true FCF yield, which is defensible given real growth and reinvestment (Qualcomm at 5.50% is the outlier worth a look, priced well below Nvidia's 1.82% but probably for good reasons). The SaaS layer is the anomaly: yields above the risk-free rate with still-decent growth, which by my discipline (I usually see 6-20% yields with something visibly wrong).

Also ran a Porter's Five Forces pass on the model layer itself (treating a frontier lab as the business under evaluation), since none of the fundamental moats visible everywhere else in the chain (fab, patent, export license) show up there. Scores halfway decent on 1 of 5 forces.

Full writeup with the FCF/yield dual-axis charts and net income vs. true FCF breakdowns: https://cavemanscreener.substack.com/p/early-isnt-wrong-pricing-the-ai-trade

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

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.

Here's a link to the full study

I've noticed that companies will announce a dilution and, almost always, the stock will immediately tank.

I got curious and dug through the data of 10,000+ dilution events since 2016 and over 3,000 discrete dilution events since 2021 (pulling filing to the exact day of dilution announcement), and came to some interesting findings.

The hope is to use this information to avoid buying companies with immediate dilution risk or maybe even develop a trading strategy to profit from them.

Forecasting Diluters

Running a logistic regression (I won't bore you here; see the article for methodology), I found that diluters can be forecasted with pretty high accuracy.

Timing

Using the post-2021 data, I found that:

• Dilution events spike in January, July, and October.
• Dilution event happen earlier in the quarter
• Dilution events typically happen before or during earnings releases.

Post Dilution Performance

Using the post-2021 data, I found that while there's an initial drop immediately following a dilution announcement, the real drag is a long term bleed in the stock price.

Using the full post-2016 data, I looked at the distribution of returns following a dilution event. The return profile for diluters is a fat left tail - with heavy negative expected returns over a 6 month time frame.

Some work still needs to be done for the performance part - I'll need to correct for size and quality, probably (i.e., expected returns for Google diluting look much different than LUNR). I also want to look at different permutations of performance characteristics - for instance, can P(dilute) be used as it's own quality metric?

Current At-risk Companies

Some of the big-name at-risk companies that were flagged in this screen (using Q1 data) were $SPCE , $AMC , $HTZ , $LUNR , $CRWV . All of these carried a 30%+ probability of dilution for Q3, all diluted, and all are down double digits since they diluted (two as much as 60% since the dilution event).

Big Takeaway

At the very least, I think this metric can be used to inform our timing decisions for stocks that we want to buy. Notably, the bleed typically continues long after the dilution is announced or occurs, so there's no rush to buy after the initial drop.

I think this probably can be used as a trading strategy, but expected returns currently are driven more by the junky-ness of the company rather than pure dilution announcements. So more work to be done on the trading front.

noticed something weird on TPL and wanted to see if anyone else has flagged it.

Horizon Kinetics has been buying one share of TPL almost every trading day for the last month. not one thousand, not a hundred. one share. 19 buys in 20 days, 18 of them exactly 1 share, prices $351 to $435. total capital deployed maybe $8k.

read that any way you want. my read is its a 10b5-1 plan set to buy N=1 daily, which turns every Form 4 filing into a public conviction signal without burning any real capital. horizon already owns millions of shares, so they arent building a position, theyre publishing one.

the prior 90 days at TPL shows the same shape. 62 buys, mostly horizon, mostly small. so this has been going on for months.

what makes it interesting to me is standard screeners flatten this into "horizon bought $8k" which tells you nothing. the signal is in the size distribution not the dollar sum.

writeup with the detection code (js, about 30 lines) if anyone wants to run it against their own watchlist: https://edgarkit.com/guides/spotting-scheduled-accumulation-programs

curious if anyones seen this pattern at other names. Ive spot-checked a handful but not systematically screened yet.

disclosure: i built the api that pulls this data. not shilling the signup, just sharing the pattern.

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?

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

On 3rd July, I published a detailed write‑up on Substack explaining why the Victoria PLC 2028 bonds, trading at ~20 cents, offered one of the most asymmetric opportunities in UK credit for investors willing to engage with a complex situation.

Five days later, the timing proved unusually fortunate: Victoria has now proposed a deal to bondholders, and it is materially more favourable than what the market had priced in.

Under the proposed terms, the expected return in under a year is roughly 2.5–3×, depending on final participation and settlement mechanics. Despite this, today’s trading didn’t show the dramatic price reaction one might expect — although liquidity is now naturally constrained because around two‑thirds of holders have already signed up to participate in the deal. That makes entering fresh positions more challenging

Both the original write‑up and today’s deal analysis are available to read for free on Substack - http://substack.com/@boringcorners

SK Hynix lists on Nasdaq this week. $28 billion raise. Biggest foreign listing of its kind in years.

Most people are writing about accessibility. US funds finally get a way in. One analyst put it cleanly: the listing removes an accessibility discount, not a quality discount.

Same day Bank of America publishes a note that bothered me more than any rating on this IPO. Not about SK Hynix. About the broader pattern. High-multiple stocks gapping up like this, historically, precedes snapbacks. BofA still calling for the S&P to close the year lower than where it sits today.

I keep staring at the capacity side of this. They're building new fabs, more NAND coming online, DRAM expansion all rolling in over the next two years. Rational if demand keeps climbing. I've watched this dance before in memory chips, just not with HBM specifically, HBM barely existed as a real market back then.

The cycle worth remembering is 2016 to 2018. Cloud and server buildouts, plus a supply-side twist where manufacturers shifted capacity toward 3D NAND, which tightened conventional DRAM. Suppliers hit record margins by late 2018. Samsung raised capex over 50% year on year chasing that demand. Then 2019 hit and the industry crashed itself, the same capacity that looked essential during the boom landed right as demand cooled.

Nobody adds a fab because the cycle's turning. Everybody adds one because it looks invincible. That's always been the tell.

Here's the twist this cycle might have that 2018 didn't. Quantization shrunk models to a quarter of their stated RAM requirements. MoE architectures wake up a sliver of parameters per token instead of the whole model. KV-cache tricks cut inference memory further. None of these were planned when the GPUs shipped. They happened because someone got constrained enough to figure it out.

Worth being honest about where that leaves DRAM and HBM today: none of it has dented demand yet. Inventories are at historic lows, suppliers are screening customers for real order volumes, and HBM demand is still climbing something like 85% a year. The efficiency tricks exist. They haven't shown up in the numbers.

Storage hasn't had its turn at all. If inference keeps pushing toward longer context windows, bigger KV-cache offload, models sitting on disk between calls instead of fully in memory, NAND could become the next place someone gets clever about doing more with less. That means SK Hynix, Samsung, Micron are all building capacity for a demand curve that assumes the current way of using memory doesn't change. History says that assumption rarely survives the whole cycle.

The contrarian case here isn't just cycle timing. It's the industry capitalizing hardware at the exact moment software is historically most likely to route around needing as much of it, even if that hasn't happened yet this time. Both directions can undercut the same capex bet. Hardware on one side, software on the other.

This doesn't mean Friday's IPO goes bad. Probably prices fine. Institutional demand is real. But two things can coexist: the accessibility discount gets removed today, and capacity built during peak euphoria hits the market eighteen months from now, right when it usually does. One's about this week. The other's about what happens after everyone who needed to buy has already bought.

I wrote up a detailed deep dive on Sun Art Retail (6808.HK) - can be found on Substack (free to read) under the username boringcorners.

WHAT MAKES THIS INTERESTING

2nd largest supermarket player by revenue in China behind Walmart/Samsclub

Net Cash > Mcap

Unencumbered investment properties >2x Mcap.

Get paid to wait with 18% Dividend Yield - good reasons to believe dividends will hold up

PE owns 80% of company and current price is significantly below what they paid. Founder of PE firm has taken over CEO role

Retail Operations Stabilising:green shoots of recovery. Business is self sustaining from cash perspective (+ve operating level NOI less capex)

Interested to hear thoughts from community.

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.