The first is the FDS discipline question. I bought FDS in early 2026 when its true FCF yield was above 8% and the market was pricing it as an AI casualty. The thesis: it died of a theory, not of an actual deterioration in its business. Four consecutive quarters of accelerating ASV growth and 95%+ retention while the stock fell confirmed the mispricing. I just sold it as the true FCF yield compressed below 6% from price appreciation. The question for this community: is 6% the right sell threshold for a business with FDS's moat profile and growth trajectory, or is that too mechanical? The counter-argument for holding would be that ASV is still accelerating and the Google Cloud Gemini partnership is just beginning to flow through the numbers. I sold on the yield compression. Am I being too rigid about the methodology?

The second is the European ADR technical question. I'm starting positions in VCISY (Vinci) and ADRNY (Ahold Delhaize) based on 12%+ true FCF yields and the ECB independence macro thesis. To do this analysis I had to rewrite my screener to handle ADR filings. European companies file under IFRS through different mechanisms than US 10-K filers and the XBRL tag mapping doesn't always translate cleanly. TSM showed zero CapEx in my original screener due to a 20-F IFRS filing issue. I've partially addressed this but I'm not confident the European ADR numbers are as clean as my US-listed coverage. Has anyone here developed a reliable methodology for normalizing European ADR financials against US-listed comparables? Specifically interested in how others handle the CapEx and SBC XBRL tags for IFRS filers.

The macro thesis for the European positions if it's useful context: the ECB is more independent and more conservative than the current Fed under Bessent and Trump pressure. If US monetary policy gets looser than it should be, European infrastructure and consumer staples with pricing power and independent central bank backing are the natural hedge. VCISY at 12%+ true FCF yield has long-dated concession contracts on toll roads and airports. ADRNY at 12%+ is the parent of Stop and Shop, Giant, Food Lion, and Albert Heijn — founded 1887, survived two world wars. Both pass my true FCF yield screen comfortably.

Full piece with the return table and regime framework: https://cavemanscreener.substack.com/p/investing-regime-changes-my-successes

I don't own Meta.

Meta spent $31.1B in Q2 and printed $784M of free cash flow. Stock fell about 8.6% the next day. That’s the print people marked. They also split the ad line. Impressions +14%, down from +19% in Q1. Price per ad +12%, second quarter in a row. Daily users only +3%. Ad revenue still +27% to $59.4B. The extra money is coming from charging more per ad, not from finding more people.

Full-year capex is $130–145B against $72.2B last year and they didn’t guide 2027. In a business where users grow 3%, that spend only shows up if they can keep charging as impressions slow. Two quarters of +12% is not a payback. What would break it: price per ad under 8% for two quarters, impressions already in single digits. Then they bought reach they can’t charge for. Could also just be a tighter auction. They don’t publish ROAS, so I can’t split a smarter match from scarcer inventory yet. Did the $31B buy a better ad, or more of them?

https://www.investmoat.com/research/metas-capex-has-one-receipt

Substack writeup on Sigenergy (free to read)

For a few years now the hyperscalers have been spending agressively, but it seems to me like spending this money is actually the easy part of the equation.

If we add up the contracted, not yet delivered cloud demand (Remaining Performance Obligations - RPO) of the hyperscalers (Microsoft, Oracle, Google and Amazon), we get a sum of about $2.3 Trillion (We still don't know if this will ever get paid but still RPO is a better estimate than a forecast & a big portion of it is from frontier AI labs like OpenAI and Anthropic). In order to serve this massive RPO, MSFT, GOOGL, AMZN, and META are spending about $660 Billion a year, moving from asset-light software companies into owners of physical plants, real estate, and equipment.

The real battle for big tech isn't buying servers, but rather securing 24/7 power. That kind of energy commitment completely breaks traditional budgeting. As JPMorgan put it, "money can't buy you electric power" when a project's start date depends on a years-long grid-connection queue. Out of these hyperscalers, Oracle is the outlier, it carries net debt of about 3x operating cash flow (an order of magnitude above the others), and its free cash flow has gone negative. Amazon's trailing free cash flow also crossed into the red.

I have to say, these companies remain among the most cash-generative business ever built, and they're funding roughly half the build from cash and the other half from debt markets. T. Rowe's Dom Rizzo argues the funding gap is "not that big" against balance sheets like these, and that unlike 1998 (the dot-com bubble) the fundamentals are still accelerating.

Now the interesting part is that if we follow the money trail down the supply chain, we can see exactly who is extracting the value. It starts with the regulated utilities like Southern, Duke and Dominion who are accelerating capex to build grid capacity. These guys face huge regulatory hurdles and political blowback from residential ratepayers over increasing electricity bills. To go around the regulations, NextEra for instance is using a hybrid strategy by restarting a nuclear plant (Duane Arnold) for Google.

Because the public grid is too slow, big tech are chasing independent producers like Constellation, Vistra, and Talen energy. These companies have 24/7 power and they spend almost nothing to expand. Amazon and Meta are locking up deals with Vistra, Microsoft contracted to restart the Three Mile Island in Pennsylvania, and Google signed for small modular reactors with Kairos.

One layer down, we arrive to the equipment makers like Vertiv, GE Vernova, Eaton and Caterpillar which are facing an increasing days inventory outstanding (DIO). Usually this is looked at as constraint on these companies, but as the gross margins also keep on rising for some of these companies, even hitting a record for Vertiv and GE Vernova, this is pure pricing power for them.

There are so many other layers that plays into this like, cooling systems, precision HVAC, advanced packaging & foundry equipment, copper mining & refining for power grids, etc etc. that won't fit into this single post.

The takeaway is big tech is bleeding money and the wealth is pooling at the bottom with nuclear operators collecting and hardware manfucaturers hitting record margins while the the payers wait for delivery.

If you're interested to read more, I have the full write up with plots & figures on my free substack:

https://secaura.substack.com/p/the-two-trillion-dollar-bottleneck

Burry's recent work swaps the GAAP stock-comp charge for what shareholders actually lose. The formula is Ω = C + V. V is the market value of shares delivered to employees, C is withholding tax net of option proceeds. Owners' earnings are N + G − Ω, and ΔE is how much of reported profit survives.

The problem is V = T·(W+ΔS)/W. It needs W, the shares repurchased, which almost no company tags in XBRL. But P = T/W, so the W terms cancel:

V = T + P·ΔS

You only need the average share price. That is what makes it automatable. The identity is exact. For P the tool uses the year's average market price, which is how his later write-ups define it and what he uses for companies with no buyback; in his NASDAQ-100 study he used the buyback program's own average price where there was one, and the two differ a little. On Salesforce that difference is worth about four points of ΔE, see below.

I built it against SEC EDGAR and checked it against his numbers. Alphabet's V matches all ten published years to the dollar. Pooled ΔE comes out 88.68% against his 88.7%, Meta 83.35% against 83.35%, the NASDAQ-100 overstatement 19.77% against 19.78%. There is a self-test button in the sidebar that runs those checks and a few hundred others.

I should say up front that the method is Burry's and the code was written with an AI assistant. My part was deciding what it should do, running it on real companies, and checking every figure against the filings by hand. I mention it because I am not going to pretend otherwise, and because it is relevant to what I am asking for at the end.

The valuation half is messier. He publishes the 15% required return, the two-model structure (a multi-stage model with a terminal value, and a multiple on year-15 owners' earnings, blended by confidence), and for each of his five moat tiers the stage lengths, the fade multiplier, the terminal growth cap and the debt capacity. Two inputs he has never published: the exit multiple and the blend. I tried to solve for them from his published IV15 values and the solve is degenerate, so you cannot recover them; the tool's are calibrated so that the growth needed to reproduce a published IV15 matches the company's actual growth, with Adobe as the anchor. Given his owners' earnings figure and growth for Salesforce, the arithmetic reproduces his $69.81 within a dollar, and that is a self-test. On the tool's own seeds it lands well above him, because the seeds are not his judgement. Paylocity does not reconcile at all: he says in the article that he applies a judgement discount to its ΔE, and I cannot recover the size of it.

Where I differ from him, and why. His pooled figure for Salesforce is 54.7% over eleven years. The tool says 77.6% over nine. I have his table next to the tool's and can account for the whole gap. Net income, GAAP SBC, buybacks and the employee-plan cash line agree to the dollar in every year. About four points are the window: he starts in FY2016 and includes FY2020, the tool drops both (FY2020 because the share count jumped 16% on Tableau; he handled the same year by netting the Tableau shares out by hand). About four points are the share price: he uses the buyback program's own average price, and Bloomberg's annual average where there was no buyback; the tool uses the year's average market price throughout. The remaining fourteen points are acquisition shares. His table sets aside the Tableau and Slack shares but charges the MuleSoft shares of FY2019, and the FY2017 deal shares, as if they were compensation. The tool deducts every acquisition issuance the filing tags, which is what his own rule says to do. Over the last three years, with no acquisition shares in play, we are four points apart, 93.9% against his 90.4%, and that is the share price. I would rather show the difference with its causes than a number tuned to match his.

On EDGAR being garbage. He raises it himself. His complaint is that filers bundle line items, so the buyback line often carries RSU withholding tax as well as actual repurchases. That inflates T and zeroes C. It breaks his calculation because he gets the price from P = T/W, so a contaminated T inflates the price applied to every share issued. This tool never uses T/W, the price comes from the market. And because Ω = C + V, withholding that ends up in T instead of C overstates V by exactly what it understates C by. The error cancels. I checked the algebra.

What does not cancel: filers who report a single net proceeds line, and shares issued for acquisitions or offerings that XBRL does not tag separately. The app flags both instead of guessing. It is not the same as reading footnotes by hand and I am not claiming it is.

What it refuses to do. The rule I gave it is that it must never print a number it cannot stand behind, so it refuses out loud instead. Some of the refusals you will hit:

• Multi-class share counts. Berkshire reports a diluted average in Class A equivalents. The tool reads 1.6M shares, notices the market cap that implies is impossible, and refuses rather than printing a valuation.
• IFRS filers. Only the net income tag is IFRS-aware. Foreign filers get a banner and the valuation is disclaimed.
• Banks, insurers and REITs. Detected, return on capital withheld, verdict forced to amber. Investments backing policyholder liabilities are not shareholder capital and the ratio does not mean what it means elsewhere.
• Stale balance-sheet lines are reported, not repaired. If a debt or equity line stops years before net income does, it says so and states the size of the disagreement, rather than carrying the figure forward or zeroing it. Neither is conservative in general: the direction flips between assets and liabilities.
• A loss year on a profitable record. Crocs reported a net loss for 2025 after a $738M non-cash write-down, on a business that earned $950M the year before. The tool seeds owners' earnings from the five-year median instead of from the loss, and tells you it did. Valuing Crocs off the write-down year would be a verdict on the charge, not on the business.
• ΔE above 100%, which happens when buybacks retire more stock than a year issues, is shown as measured but never projected forward. Fifteen years of handing owners more than the company earns is not a business model.

Every page carries an "assumptions used" block you can paste if a figure looks wrong, and a tag panel naming every XBRL element it read or failed to find. If a line you know exists reads fewer years than net income, that is a bug, and the tag name is usually the whole fix.

Known gaps, stated so you do not have to find them. Cash-flow lines that stop early (a withholding line that ends while stock comp continues) show up in the tag panel but are not yet flagged in the notes. A company that changed its fiscal year end reads as having a missing year, because years are labelled by the calendar year they end in; Build-A-Bear does this and the note now says which of the two it might be, but cannot yet tell. Up-C structures report the parent's slice of income against a full share count. And the second page, which implements Mayer's 100-bagger criteria, is newer and less tested; feedback on it is welcome, but the Tragic Algebra page is what I am asking you to break.

What I am asking. Break it. The arithmetic has been checked by hand, row by row, against the filings on a few dozen names, including every company in his articles. Pick whatever you like; the ones I have not run are the useful ones. Every new company I run finds something. The last few days turned up an Apple share count restated by 3.5 instead of 4, a growth ratio computed against a negative denominator, a note that credited a $3M token buyback for a ΔE above 100%, and a microcap whose stock comp was rounding to zero in the table. All fixed, all with regression tests. The next one is in there somewhere, and it is more likely to be a note that misdescribes what it found than a wrong number.

Particularly useful: a company where you already know the answer and it prints something else. Paste the assumptions block with your comment and I can usually see the cause from that.

On the break-even in the title. ΔE is not a one-off haircut, it applies every year, so intrinsic value per share retains ΔE^t. Growth lifts value by (1+g), dilution cuts it by ΔE, so the net is ΔE × (1+g). Set that to 1 with 15% growth and you get ΔE = 1/1.15, about 87%. Below that a company needs 15% reported growth just to stand still. The NASDAQ-100 is at 83.5%, so 15% growth there compounds intrinsic value per share at −3.99% a year.

Tool: https://tragic-algebra-analyzer.streamlit.app/Tragic_Algebra_Analyzer
Code: https://github.com/ChenFindling/tragic-algebra-analyzer

Happy to be told where the method is wrong.

I’m learning equity research and decided to make Haleon my first proper deep dive. I’ve tried to approach it like an actual institutional-style report: investment thesis, five-year financials, a DCF with defended assumptions, risk factors, catalysts, and, ultimately, a clear BUY/SELL call.

I’ve also gone through the annual report, MD&A, risk factors, and notes to the accounts rather than just relying on the headline numbers. Basically, I tried to read the stuff people usually skip.

Now I’d really appreciate some brutal, honest feedback from people who actually know equity research.

A few things I’d specifically like you to tear apart:

1. Valuation I’m using a 7.5% WACC and 3% terminal growth. Are those assumptions actually defensible for Haleon, or am I being too aggressive/conservative?
2. Thesis Is there anything important I’m missing? Particularly risks, catalysts, or something that could fundamentally undermine the margin-expansion story?
3. Structure Does the report resemble a proper equity research report, or is there too much unnecessary stuff and not enough of what actually matters?
4. Data Do any of the numbers look suspicious or inconsistent? Are there particular figures or sources I should cross-check?
5. Overall quality What would I need to change to take this from “student learning equity research” to something that looks genuinely institution-ready?

The basic thesis is:

Haleon owns brands such as Sensodyne, Advil, Panadol and Centrum. Revenue growth has been relatively weak, but margins have expanded significantly, with operating margins around 22.5% and gross margins around 64.8%. FCF is around £2B annually, while net debt/EBITDA has been coming down toward 2.6x.

My argument is that the market is overly focused on the revenue growth miss versus management’s 4–6% medium-term target and is underappreciating the margin expansion, cash generation and potential H2 2026 recovery.

My DCF gets me to roughly $11.50 versus a current price of about $9.70, so I’ve landed on BUY.

I’m still learning, so I’m much more interested in someone telling me where the analysis is wrong than telling me what I did well. Haleon_first_equity_report

I don't own Coinbase or Strategy. They're not a bitcoin position.

I keep seeing both talked about as if that's how you own bitcoin. I don't buy it.

Strategy's August 17 8-K is the one that made this obvious. 840,447 BTC. That week they bought none and sold none. They sold $334 million of stock and sent the cash to a reserve, buybacks, and preferred dividends. That's a company deciding what to do with cash. The coins don't get a vote.

Coinbase Q2: record trading share, revenue still down, GAAP loss. Something like 88% of net revenue wasn't even BTC spot. A better on-ramp is not the coin.

https://www.investmoat.com/research/bitcoin-and-the-crypto-exposure-slot

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

I own GEV. Not because it's an AI-power stock. Because they already sold the turbines.

Last quarter the slot book went from 100 GW to 116. They signed 20 GW, converted 10, actually shipped 3. Same customers are buying the transformers from them too. Once that machine is sitting in the plant you're not ripping it out for someone else.

People keep lumping this with Constellation, Vistra, and Talen. Those are 20-year power contracts. Different thing. Talen is one Amazon plant. I'm not in that.

Wind is ugly. Lost $657 million in the first half. They only guided about $400 million of losses for the whole year. If the slots stall at 116 and Wind stays like this, I was early.

https://www.investmoat.com/research/ge-vernova-and-the-ai-electricity-basket

This was the natural close to a series of articles that I've written about dilution. I went in with the intuition that dilution isn't an automatic drag on stock performance, but the practitioner in me constantly saw companies announce dilution which was followed by an immediate drop in share price.

So I decided to actually look into it and pulled the data for all dilution events for the past ten years (2016-present).

The findings were that dilution doesn't have much of a short term impact on share price...especially as you go up in market size.

Micro-cap diluters pretty much always trailed their benchmark, but I suspect that has more to do with business quality.

When inspecting a single company, I think the question to ask is will the capital raised earn the cost of capital for the business?

If it's a business expansion move or already baked into your analysis (i.e., pre-cash flow company that was expecting to raise capital to move the business along), then I think it'll have less of a negative impact on share price.

Conversely, if it's a business that's just trying to stay afloat, I imagine that's when share price really drags down.

These are speculation on my part, though. I didn't do any work to explore causal factors.

To buy all those chips, they borrow huge amounts of money, using the chips themselves as collateral. CoreWeave now owes around $72 billion and lost $626 million last quarter, most of it just interest on the debt. A lot of this debt gets rated "safe," even though CoreWeave itself is rated junk. The loans are backed by rental contracts with companies like Microsoft. So the rating is really about Microsoft's ability to pay.

Take something risky, wrap it with something solid, and call the whole thing safe. Except chips are worse collateral than houses. A house holds value for decades. A GPU can lose most of its value in three years when the next one comes out. You're lending against something that's melting. Isn't this basically the same move that blew up in 2008 with mortgages? Is this smart financing, or 2008 with chips instead of houses?

I noticed the nuclear tape still prices Cameco, Kazatomprom, NexGen, and Uranium Energy as one uranium-spot trade. I don't think they are.

Cameco is already contracted for more than 28 million pounds a year through 2030. Q2 realized US$67.79/lb against US$85.18 spot. That's old ceilings, not a scarcity premium. KAP realized $66.61 the same quarter. Similar price, no Western lock-in.

NexGen has 10 million pounds of offtake against up to 30 million pounds a year of nameplate. UEC sold nothing in Q3 FY26 on purpose.

The sort is whether a name is already inside a utility fuel plan, or still a call on the pound. Realized price this quarter does not settle it. Both producers are lagging spot.

https://www.investmoat.com/research/cameco-and-the-uranium-beta-trade

Mine. Not advice. Is Cameco in that basket because of the realized price, or are NXE and UEC the actual spot trade?

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.

I recently saw that Alphabet reported $112.2bn of net income last quarter while the actual business, ads and cloud, earned $40.8bn of it, so I looked into where the rest came from. It is a $99.0bn gain on investments, and only $278m of that came from selling anything. $77,354m of it is Alphabet raising the value of stakes it holds in private companies. I asked Eli Bartov, professor of accounting at NYU Stern, who advises money managers on financial reporting and has testified as an expert in securities fraud cases, what to make of a profit built that way. He said there is a risk management could use that discretion to influence reported gains or losses and achieve its desired reporting outcomes, potentially resulting in misleading information for investors.

Alphabet can do this under ASC 321. With no share price to look up, you hold the stake at what you paid and move it only when someone else buys into the same company at a different price. So the value rises whenever a new investor pays more, and whether the company itself makes or loses money never touches your income statement. Over the first half of 2026 those markups grew $41.2bn while write-downs grew $295m, about 140 to 1. Microsoft has enough influence over OpenAI to use accounting that passes results straight through, and its OpenAI line reads minus $1.5bn, minus $4.8bn, then plus $6.5bn across FY2024 to FY2026.

Bartov also put a number on it. Taxing the gain at the roughly 19% rate Alphabet paid for the quarter, he had about 70% of net income as non-recurring, and warned investors could overestimate its sustainable earnings power and bid the stock above fundamental value. He gave the fair counter too, that accounting rules have to work across thousands of companies and will never perfectly fit one, so the adjustment is the reader's job. Figures from the 10-Q filed 23 July 2026.

Full write-up with all figures and data: https://www.theresearchnote.com/articles/alphabet-99-billion-equity-gains-measurement-alternative

The standard PEG critique from this community usually focuses on earnings quality. I decided to make some modifications:

FEG replaces P/E with price-to-true-FCF (OCF minus CapEx minus SBC) and replaces earnings growth rate with three-year revenue CAGR. Revenue CAGR is more stable than true FCF growth as a proxy for underlying business trajectory. The tradeoff is that revenue growth doesn't capture margin expansion or compression - a company growing revenue at 10% while margins compress looks the same as one growing at 10% while margins expand.

Y220 converts the ratio into a time question: at current true FCF yield compounding at the three-year revenue CAGR, how many years to reach 20% true FCF yield? The 20% threshold is somewhat arbitrary - it's where CMCSA sits today - but the concept of expressing value as time-to-yield rather than as a ratio feels more intuitive for comparing growth names to value names on a single axis.

The scatter plot of current true FCF yield versus Y220 across the $50B+ universe is the output I'd most welcome pushback on. The methodology questions I'd want this community's view on: is three-year revenue CAGR the right growth proxy or is there a better stable series? Is 20% the right yield threshold or does it introduce too much path dependency from today's rate environment? And does the linear compounding assumption in Y220 produce systematically misleading results for very high or very low growth names?

Full piece with the scatter plots and screener table: https://cavemanscreener.substack.com/p/my-new-godfather-metric-how-long

I published a bearish valuation thesis on Match Group at approximately $37/share, with an $8–11 target over 24–36 months.

The core question is whether Hinge deserves to keep Match Group trading at a substantial premium to distressed dating-app comps while the rest of the portfolio contracts.

A few numbers:

Match Group payers peaked at 16.55M in Q3 2022 and have fallen to roughly 13.3M.

Tinder payers are approximately 22% below peak.

Revenue per payer has risen substantially during the same period, allowing revenue to hold up much better than the customer count.

That looks increasingly like harvesting a shrinking payer base rather than a conventional growth transition.

Hinge is the offset. Revenue is growing strongly and monetization is excellent.

My concern is that much of the incremental runway is now geographic. Core-market user growth is relatively flat while expansion markets increasingly drive growth.

I think Hinge's current revenue-per-payer premium reflects geographic mix more than a permanent product characteristic.

The valuation is where this becomes interesting.

MTCH trades at roughly 8.8x adjusted EBITDA while Bumble trades around 2.5–3x on comparable economics.

I am not assuming Match converges all the way to Bumble.

My base case gives Match roughly twice Bumble's multiple: 4.5–5.0x 2029 EBITDA.

At approximately $950M of modeled EBITDA, ~$3B of net debt and a substantially reduced share count from continued buybacks, that produces roughly $8–11/share.

The capital structure matters enormously. The debt claim stays fixed while the equity absorbs the enterprise-value decline.

The report also includes a flat-EBITDA sensitivity because I don't think the short requires heroic operating deterioration. A large portion of the downside is simply multiple convergence.

I spend a lot of the report on the underlying dating-market structure, but the financial case is deliberately written to stand independently of it.

Full thesis, model, steelman and falsifiers:

https://dljlevfin.substack.com/p/the-undisclosed-denominator

Would welcome a serious bull case, particularly around what durable multiple you think MTCH deserves if Tinder stabilizes but Hinge's geographic mix causes blended monetization to compress.

Below is a great post on the financial games promoters are playing in the financing of hyperscale data centers. The author teases apart the various aspects of the deal structure and explains them coherently. IMO, this is a must-read for anyone interested in this space:

https://shanakaanslemperera.substack.com/p/the-clocks-on-one-building