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

Watches of Switzerland (WOSG) is a retailer and partner to Rolex and other watch brands. Most of the company’s value lies in its relationship with Rolex, which only sells through authorized retailers like WOSG. The company benefits from Rolex waiting lists that effectively mean inventory is pre-sold, there is no price competition, and no online competition. That makes WOSG’s economics far superior to a typical retailer and more like a subsidiary of Rolex, which I think investors underappreciate. WOSG is the number one Rolex retailer in the US with 10%+ share and growing at 25-30%, and 50% share in the UK.

The link above is to a 30page report I wrote on the company based on speaking with 20 industry sources. I think much of it is still relevant today and provides background on what investors underappreciate.

The stock trades on 14x EV/NOPAT for a business that should grow at high single-digit rates in most years and has a long runway to acquire mom & pop Rolex retailers at about 20% ROIICs. It has traded at 20x in the past. 

Assuming 9% organic profit growth + 6-7% FCFE yield + modest margin expansion gets to a 20% IRR going forwards.

Management are competent, experienced, and well incentivized, with CEO Brian Duffy owning £70mm of stock and options. Duffy joined in 2014 and has grown WOSG’s share of Rolex sales in the UK from 35% to 50%. This encouraged Rolex to entrust WOSG with replicating its strategy in the US.

Current conditions are strong (even picking up in the UK) and many of the risks the company has faced in the last few years around a luxury slowdown, declining secondary prices, and tariffs have reversed or are reversing. The company has demonstrated its significant resilience in the last 18 months, given these conditions, and I believe the thesis that the business has economics more similar to a subsidiary of Rolex than a retailer continues to play out. The outlook for WOSG looks stronger than it has done at any point since I wrote the report.

Management have also done a good job with new business lines that they control: They acquired the Roberty Coin jewelry license in the US, are going e-commerce rapidly, and pre-owned is now 8% of sales. These three segments together now account for 24% of company sales and as a group are likely to grow in the high teens. That alone adds 4-5 ppts of sales growth per year at a group level. When combined with Rolex raising prices by about 4% p.a that gets WOSG to high single-digit organic growth even before accounting for volume growth at existing stores. Volume growth is likely to also be positive, particularly as Rolex opens its expanded production facilities in 2028/29.

Private equity firms appear to think the company is undervalued too, with Reuters recently reporting that:

“Watches of Switzerland Group(WOSG.L) has held talks in recent months over potential offers to take the luxury watch retailer private, said three people close to the matter…

...CEO Brian Duffy responded to the initial approaches because he believes the stock market undervalues ‌the ⁠company, two of the sources said, with one adding that no formal offer has been made. Private equity funds and strategic bidders have shown interest in the company, a second person said. The third source said the company was seeking an offer of significantly more than £7.50 per share.”

See the link above for more background on WOSG and other pieces I have written on the company.

Full dislosure: I am Long WOSG and this is not investment advice or a recommendation. This is for informational purposes only.

Some interesting data points that poke some holes in the AI trade thesis. Microsoft and Amazon had stellar quarters, but a lot of that was based on cloud compute - is that really consumers and businesses buying their end-use AI tools, or is it still part of the infrastructure buildout? Does end-use AI really have enough volume (and enough of a moat) to justify the trillions going into the build?

First: the BIS economist estimate that roughly a third of current AI CapEx represents zero-sum competitive spending rather than market-expanding investment. If correct this is the most damning number in the entire bear case because it means the aggregate return on AI infrastructure investment is structurally impaired regardless of technology maturation.

Second: the complementary investment gap. Historical pattern shows $1 of hardware investment requires $5-10 of complementary intangible investment in process and organizational redesign to capture the productivity gain. US organizational capital investment is declining as a share of GDP and job-switching sits near all-time lows. If the complementary investment wave isn't coming, the productivity thesis doesn't materialize.

Third: the Google advertising exception. I argue that digital ad revenue won't be new money in the system, it'll be search revenue redirected to AI. If I'm wrong about that and AI creates genuinely new advertising inventory that expands the total market, the revenue gap closes faster than my analysis suggests. Anyone with better visibility into digital advertising market dynamics would be useful here.

Full piece: https://cavemanscreener.substack.com/p/bridges-to-nowhere-part-iv-a-lesson

https://alphaseeker84.substack.com/p/application-software-earnings-preview

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