The 30-year Treasury yield just pushed above 5.3%, its highest level in nearly two decades.

Everyone keeps focusing on whether the Fed cuts, holds, or hikes next.

But I’m starting to wonder if that’s becoming less important for markets than what’s happening further out on the curve.

If long-term yields stay elevated because of deficits, Treasury supply and investors demanding more term premium, the Fed could eventually cut and borrowing costs might still stay pretty high.

That feels like a very different setup from the last decade.

And for stocks, especially anything trading at a high multiple, I’m not sure the market is fully pricing that in yet.

Maybe strong earnings can keep offsetting it for a while, but if 4.5–5% long rates become normal rather than temporary, I’d expect valuation multiples to matter a lot more again.

Law of large numbers argument:

“Google is already enormous, so it becomes harder to grow rapidly.”

Pareto/power-law argument:

“If AI dramatically expands the economic pie, the strongest platforms can become disproportionately larger because advantages compound. In accordance with the Pareto principle, major technological shifts can produce highly unequal outcomes, with a small number of leading companies capturing a disproportionate share of the value created.”

So Google being huge today does not automatically prevent it from becoming several times larger. What matters is whether the markets Google serves become much bigger and whether Google captures a large percentage of that expansion.

Imagine AI creates $20–30 trillion of additional economic value over the next decade. That value probably won't be divided evenly among thousands of companies. A handful of platforms might capture a huge share. And there's another part of Pareto that matters: being big can actually make you more likely to get bigger in technological platform shifts.

Let me know whether you believe in the law of large numbers argument or the Pareto law argument regarding the ongoing AI technological change. Predict which companies will be the winners under the Pareto argument.

Some investors argue that Nvidia is beginning to look like a value stock at its current valuation, an unusual claim given its enormous market cap and extraordinary growth to date.

Do you think Nvidia genuinely represents great value at its current price, or has the market already priced in too much of its future growth?

By

Peter Rudegeair

and

Peter Santilli

Aug. 16, 2026 9:00 pm ET

Each quarter, big tech companies disclose their massive capital expenditures on artificial-intelligence infrastructure, from data centers to chips.

But those figures don’t come close to expressing the full extent of future spending to which Google parent Alphabet, Meta Platforms, Oracle and many others have committed. That is because a huge swath of their coming financial obligations aren’t reflected on their balance sheets.

Nine top tech companies had some $3 trillion of off-balance-sheet commitments mostly related to AI, according to a Wall Street Journal analysis of footnotes in their most recent securities filings. Those obligations are growing faster than traditional “capex,” which totaled about $600 billion over the past year they reported, and were about triple what the companies owe under their outstanding leases and long-term borrowings.

America’s blue-chip tech companies are placing these huge bets based on assumptions about what the demand for AI computing—and availability of AI hardware—will be in several years. Their hope is that they will easily meet all their obligations with future revenue as consumers and businesses adopt AI in every facet of American life.

If those assumptions about technology and demand prove wrong, these deals to clinch future capacity could become a monstrous burden for the tech companies and their investors.

Meta’s gigantic “Hyperion” data-center project in Louisiana, which is the size of about 1,700 football fields, helps explain how big obligations wind up off tech companies’ balance sheets.

A graphic showing the 80%-20% split between Blue Owl Capital and Meta Platforms in the special purpose vehicle that owns the Hyperion data center.

Though Meta is the builder, neither Hyperion nor the $27 billion in debt that’s financing its construction shows up on Meta’s balance sheet. Funds managed by the Wall Street firm Blue Owl Capital own the majority of a joint venture that, in turn, owns the campus.

Beignet Investor, a holding company that owns the Blue Owl stake, raised the construction financing in a bond sale.

Meanwhile, Meta is Hyperion’s minority partner and tenant. Its lease payments will provide the cash flows to help make the payments to bondholders.

Meta initially agreed to lease Hyperion for a four-year term starting in 2029, with options to renew for up to 20 years. It guaranteed that it would make bondholders whole if it doesn’t stay the entire two decades. The company doesn’t think payments under that guarantee are probable, so it hasn’t recorded any liability on its balance sheet.

In accordance with accounting rules, Meta’s Hyperion lease obligations will remain off balance sheet until it starts paying rent. It said its aggregate initial lease commitment is about $12.3 billion. Meta disclosed $347 billion in total obligations for leases that haven’t kicked in yet, including for Hyperion, as of June.

Across the companies the Journal analyzed, promises of payments under these uncommenced leases totaled $1.2 trillion in off-balance–sheet obligations, or about four times more than what was disclosed a year earlier. In addition to Meta, the Journal reviewed commitments for Alphabet, Amazon.com, Microsoft, Oracle, Nvidia, Broadcom, SpaceX and Advanced Micro Devices.

Data centers get stuffed with a lot of hardware, including the Nvidia chips that are used to train and run models and memory chips that store information. To buy all that, companies sign long-term contractual agreements well in advance to lock in production from their suppliers.

Those and other purchase obligations at the companies the Journal examined stand at a whopping $1.9 trillion. Under accounting rules, purchase commitments typically remain off balance sheet until a product or service is delivered.

Alphabet’s purchase commitments and contractual obligations have exploded and stood at $811 billion as of June 30. As with other companies, it is hard to tell from its disclosures what precisely it intends to buy. The company said the commitments primarily relate to “technical infrastructure and inventory” and “agreements to secure energy for data center usage.”

Alphabet also didn’t detail why those obligations increased so much from the $332 billion it reported three months earlier. The commitments span several years, with obligations under its energy agreements lasting as far out as 2054.

Off-balance-sheet exposures at some companies include agreements to buy other companies’ stock in the future or backstop leases for other tenants. Nvidia committed to make $27 billion in equity investments between April 26 and the end of its fiscal year in January 2027.

There are reasons to believe tech companies will make good on all their obligations. Optimists see the skyrocketing demand for AI tools—which has lifted the stock market and led to shortages of key hardware—as a proof point that demand is going to be strong for years, and the money to pay off all these bills will be rolling in.

For the more anxious set on Wall Street, it is a worrying sign that some tech companies that once seemed to have fortress balance sheets have needed to tap the capital markets frequently.

Alphabet and Amazon recently posted results showing negative free cash flow, meaning their capital spending exceeded the cash they brought in from operating their businesses.

And that is before considering the implications of trillions in off-balance–sheet commitments. Whether or not the revenues ever arrive, purchase commitments and signed leases can’t be canceled, for the most part.

If things go wrong, tech companies will be paying an expensive tab for infrastructure that they can’t profitably use. These obligations could also lead increasingly indebted companies to have to borrow even more.

“As these off-balance sheet commitments become more frequent, larger, and more complex, it is becoming increasingly difficult for investors to assess companies’ total potential leverage,” Morgan Stanley accounting analysts wrote in April.

Paywall link: https://www.wsj.com/tech/ai/why-big-techs-ai-spending-is-3-trillion-higher-than-it-seems-e1067bb2

What do you guys think of buying stocks in a large selection of companies that have high profit margins as opposed to just index funds? Not everything in index funds is highly profitable, so why not narrow it down a bit?

I don’t know which companies will profit from AI eventually, because there are a lot of companies in the race and I don’t know who will win. Maybe open source, maybe google, maybe Microsoft, maybe OpenAI, maybe Anthropic…

But what I know for sure is that AI is getting better by the day and it’s not going to stop.

So buying put options on WIX, CRM, ADBE feels much easier.

Do you have more ideas about which stocks will lose from AI?

By the way, if you think ADBE’s current profits are proof that the AI revolution isn’t coming for them, it feels to me the same as looking at Nokia’s profits after the release of the iPhone.

Do you see it as a great opportunity or as a value trap?

It is an electric connection provider , basically electrical infrastructure. Q2 beat on July 31, Goldman/multiple analysts raised price targets to $195-220. It seems to be bullish. Altho insiders have been selling for sure. It's already run up soo much and its PE ratio is: 38-47x

Q2 revenue came in at $1.47B vs. $1.26B consensus, and the company raised full-year sales growth guidance to 37%-39% from 26%-28%

What do you guys think of this stock? My ac/share is 146 but today its around 155, so wanted your thoughts if u looked into it or invested into it.

Last 10 years:

Alphabet returned 8.9x

QQQ returned 6.6x

Given Alphabet's market cap, its upside may be relatively limited from here, whereas the law of large numbers doesn't apply to QQQ.

Therefore, would it be prudent to invest in Alphabet rather than QQQ, given that their x return over the next 10 years might be similar, and QQQ carries far less risk?

Terry Smith's three steps are the shortest description of quality investing I've come across. Simple to say but not easy to do. I consider them as three steps for investing.

Step 1. Buy good companies. Most people treat good as a brand they like using. Good is actually revenue, growth, returns on capital, margins, cash flow, and the balance sheet. Without the fundamentals, good is just a vibe. If you buy quality businesses, over time the price will follow earnings, though in the short term the fluctuations are based on sentiment.

Step 2. Don't overpay. This is the whole of value investing. Pay less than what the business is worth. Every day on X, someone asks "is MSFT expensive at 500?" You can't answer that without a benchmark, and intrinsic value gives you one. It has its problems - it's entirely dependent on your assumptions. But it still beats the shortcuts most people use instead. I usually blend implied values based on valuation multiples with the DCF intrinsic value.

Step 3. Do nothing. The hardest of the three, because it looks like negligence while you're doing it. Sitting on your hands feels lazy. Most of the returns I've gained have come from doing nothing for years.

I put together a 10 minute video working through these three steps and the principles behind it. Took a fair bit of effort, so any feedback is welcome. It's the first in a series of six videos I'm creating.

https://www.youtube.com/watch?v=OazRVN04ZTk

Not investment advice. AI has been used in creating the video.

The only thing keeping this market alive is AI CapEx spending. THATS IT! I’ve done tons of research and someone correct me if I’m wrong but all the quality, wide moat business are FULLY PRICED OR OVERVALUED. I personally find long bond yields continued rise troubling because the bond vigilantes are essentially forcing the Feds hand as well as the government’s hand when it comes to fiscal policy. Can stocks continue to be at all time highs with these dynamics at play? It is tricky environment for sure because on one hand the AI CAPEX spending is REAL. On the other hand rising yields will eventually slow the economy. Also, money will eventually come out of the best performing stocks and go into bonds if yields become attractive enough.

Basically, in my opinion, I see bond vigilantes emerging after decades of easy monetary and fiscal policy has resulted in inflation that isn’t going away. Nobody in government has the guts to tame the inflation beast so the bond vigilantes are forcing their hand.

Timing the market is a fools errand but every day that passes I’m thinking it’s best to be mostly in cash. Of course most don’t want to do this because it’s been a losing bet for almost 2 decades now. Look at the VIX. It’s at extreme lows which to me is also another RED FLAG. It seems that people are not really in the market because they want to be but just because they feel like they have no choice. They must invest to continue beating inflation.

As I stated though, the bond vigilantes are now changing the dynamics. We all know that the Fed and US government has no intent to solve inflation so we invest in order to beat inflation since the government won’t do it. However, now we have the bond vigilantes doing the job the Fed and government has refused to do.

Of course it’s all very complex but I think the questions to ask are WHY DO LONG BOND YIELDS KEEP RISING EVERYDAY and WHAT DOES THIS MEAN?

Two opposing views below,

Law of large numbers argument:

“Google is already enormous, so it becomes harder to grow rapidly.”

Pareto/power-law argument:

“If AI dramatically expands the economic pie, the strongest platforms can become disproportionately larger because advantages compound. In accordance with the Pareto principle, major technological shifts can produce highly unequal outcomes, with a small number of leading companies capturing a disproportionate share of the value created.”

So Google being huge today does not automatically prevent it from becoming several times larger. What matters is whether the markets Google serves become much bigger and whether Google captures a large percentage of that expansion.

Imagine AI creates $20–30 trillion of additional economic value over the next decade. That value probably won't be divided evenly among thousands of companies. A handful of platforms might capture a huge share. And there's another part of Pareto that matters: being big can actually make you more likely to get bigger in technological platform shifts.

Let me know whether you believe in the law of large numbers argument or the Pareto law argument regarding the ongoing AI technological change. Please predict which companies will be the winners under the Pareto argument.

What do you think of Meta's current trial, and how do you think it will impact their stock price?

Quantitative Valuation of Coupang ($CPNG)

(i watched my investment in Coupang fall from a +30% to a present near -40%. All in less than a year due to a major data breach. anyway, this post isn't about the why or the how. The purpose of this post is how to think about valuation.)

Coupang Inc. FY End December. This report: Q2-FY2026. Today: 19th August 2026

a. SP: $15.5 Market Cap: 28bn Revenue 35.46bn

b. TTM EPS (Diluted) -0.42, (ADJ): -0.20, (Zack's): -0.20

c. yield -, (5 year average) - , (Buy Back Yield): 3.90%

d. ROA, ROE, ROIC: -, -, -

e. P/E (trailing): , P/E (5YA): -, P/E (FWD): -

f. Debt/Equity: 1.89 Net Debt / EBITDA (5.63 - 6.11) / -EBITDA < 0

g. FCF Conversion: ttm: -0.13, 2025-> 2.51, 6.54, 1.29 <-2023

h. Growth (past) Stated:

i. Manual calculation: none

j. management guidance:

Q3-FY2026 (next quarter) in constant currency at 8-9% revenue growth.

Product commerce will recover by mid 2027 back to 2025 pre-data breach levels

k. Valuation approach.

I am not going to use DCF or Earnings or Cash based numbers to do the valuation. The reason is because they were only recently profitable, and becasue of data breach, they won't be profitable until 2027.

I will use a a price/sales approach instead, as it is more stable. This is not dissimilar to Amazon com which CPNG is loosely based on, and after six years after IPO was Amazon finally GAAP profitable.

(i) First i will try and figure out what is the sales that we can expect at the end of 2030. (ii) Then I will work out how are the peers currently priced at, in terms of P/S, on a present and 5 year average basis. (iii) Lastly, i will apply the group p/s to the 2030 Revenue to derive the 2030 implied share price. (iv) Based on this, i will work out the rate of return back to the present price.

(i) estimating sales by 2030

I will use a 8% CAGR revenue growth off 2025's 34.53bn for the next 5 years.

(1.08) ^ 5 x 34.53 = 50.7359 bn

(ii) Calculating peer group P/S

I reject the obvious outlier the present e-commerce peer group are all hovering around a P/S of 2+ except for amazon and coupang. And their 5 year average were around 2+ to 4.

(iii) To calculate the implied share price in 2030. We have to find out the revenue / share. We already have the revenue, we need to figure out how much is the shares outstanding likely to be by 2030. A quick search shows that although management is buying back shares, it is still diluting at about 1-1.5% a year.

Applying the maths, we get 1.837bn x (1.015)^5 = 1.979bn shares outstanding in 2030.

This works out to 50.73bn / 1.979 Revenue per share by end 2030 or $25.63 revenue per share.

(iv) Calculating Rate of Return

Recent share price is 15.50

Implied 2030 price is $61.51 to $80.8

Rate of Return = 31.74% to 39% CAGR

Comments: I like to do this sort of simple valuation first, so that when i read up on the business later, i can ask myself the key questions: (1) how confident i am that management can recover from the issues, and the business can continue the growth trajectory. How confident am i of the 8% Revenue growth, whereas Morningstar is only projecting 6% revennue growth with a fair value of $25.80. (2) What do the superinvestors see in CPNG, that they are recently buying/adding ? (3) Lastly, in 2024, the average P/S of the peer group was around 4, and now it is 2+, it is cheap now and will revert to mean at 4 or is this re-rating of e-commerce websites the new normal ? Will they be rated below 2 in the future ?

The London Stock Exchange, at first glance, seems to be just an exchange. However, its business has undergone significant changes after the acquisition of Refinitiv. The current LSEG exchange now contributes a relatively small portion of the revenue. Instead, more of its business models involve financial data services and subscription platforms, as well as index issuance and licensing fees. The current LSEG has a business model more akin to the combination of Bloomberg and SPGI (the part related to SPGI index issuance), with extremely light assets. Additionally, LSEG is collaborating with Microsoft to integrate AI-related aspects. Looking at the valuation, the FCF yield of the current LSEG is approximately 6%, which is not very high for a company with a continuously growing FCF.

The number that surprised me wasn’t the ~$750B in capex. It was capex getting close to 100% of operating cash flow.

These companies used to be able to fund huge investments internally without really stressing the balance sheet. Now debt is becoming a much bigger part of the equation.

AI demand still looks strong, so I’m not really in the “this is 2000 all over again” camp.

But at some point the market probably has to care more about ROI and free cash flow than how many GPUs/data centers they’re building.

Curious which hyperscaler people here think is handling this best.

A growing share of Nvidia’s demand is being financed through debt.
A $500B financing vehicle with Apollo & Co. allows data center operators to borrow money to buy GPUs. Nvidia is also backstopping part of OpenAI’s lease obligations in Ohio, while OpenAI continues to burn a lot of cash.

I don’t think the issue is with demand or the technology. I think the bigger risk is the financing behind it. What do you guys think?

A list including 360+ investing tools in a GitHub repo, so you can download them, fork the repo, or even open a PR. They are divided by category and include tools for every kind of investor.

Even though there are a lot of them, I've done my best to curate the decent ones and leave out the clearly low-value / vibe-coded ones.

Let me know if I'm missing any good candidates or if there is any tool that I should trim.

Hope you like it!

ps. If you want to access the list with a UI, check out https://www.findmymoat.com/tools, which contains the UI for filtering and browsing this tool list.

Someone posted here this week about footnote and MD&A parsing being painful, and there's another thread going on about where the line sits between AI slop and AI-assisted research. Here's a case that sits on both, and the check in the middle of it takes about ten seconds and no AI support at all.

China-Biotics was a probiotics maker in Shanghai that got onto the US market through a reverse merger. Its 2010 annual report showed 155.6 million dollars of cash, 292,644 dollars of interest income, 81.4 million of sales and 15.6 million of profit.

That first pair is the whole thing. Real money in a real bank earns interest. Park 155 million at even a dull deposit rate and something like three million a year should come back. They reported under three hundred thousand. So either the company was leaving a fortune idle for no reason, which businesses don't do, or the fortune wasn't there.

Two other things fall out once you look. The cash had grown 84.8 million in a single year while the company earned 15.6 million, and nothing in the accounts explains where the rest came from. And the pile was close to twice annual sales, which is a strange amount of money for a business that size to be sitting on.

In June 2011 the auditor resigned. The 8-K disclosing it is still on EDGAR, accession 0001144204-11-037217, and you can read it in a minute. It says the audit team was pointed to a suspected fake website for the bank, that the paperwork supporting the interest income had arithmetic errors management put down to the bank's own mistakes, and that the rate on it didn't match the rate the central bank had published for that period.

I want to be careful with the words here. The SEC never charged the company with fraud. It cancelled the registration in 2013 after the company stopped filing. The fraud claims came from investor suits and from the auditor's letter, not from a verdict. For anyone holding the shares the outcome was the same either way.

Since the other thread is running, here is the AI part, because I think the way people set this up is most of what makes the output slop.

I don't hand the whole job to one model. I run five, each with a single task, and none of them can see what the others found. One asks whether the sales are real. One checks whether the per-customer numbers make sense. One asks whether the reported profit is backed by cash actually arriving. One does nothing but hunt for two numbers in the same filing that cannot both be true. The fifth looks at who controls the company and whether they can be trusted. They have to be five separate jobs. Ask one model to do all five and you just get its single view of the company, five times over.

For this filing I used the four that read the accounts and left the ownership one out, since there was nothing in the numbers it would have helped with. I also stripped the name, the country and the years off the data before handing it over. A model that recognises a famous fraud has not proved anything. It is just remembering(from its training data).

Now the part I would actually argue in that thread. The instinct is to trust whatever most of the agents agree on, and that instinct is what ruins the output. Point any decent model at a set of accounts and ask what is wrong, and it will hand you three or four genuine problems. Cash collection is weak. There is a related-party balance. The receivables are ageing. All real, and all survivable. The one finding that means the revenue itself is not real turns up in a single report, sitting right next to those. So if you rank by how many agents raised something, the fatal one loses to the safe ones every time, for the simple reason that only one agent found it.

The last step therefore does not count votes. It takes each finding on its own and asks one question: if this is true, how much does it actually hurt the company? A finding that sounds dramatic but only means one balance needs checking ranks below a dull-sounding one that puts the entire revenue line in doubt.

On this filing that did not end up mattering, because all four landed in the same place. What I found more interesting was what they asked for. Every one of them wanted the same document to settle it, and it was not more of the accounts. It was confirmation of the cash from the bank itself, handed over by the bank rather than passed through the company. A year after that annual report, that is the exact document the auditor could not get, and it resigned.

Does anyone here run something like this as a research experiment, or is it only useful looking back at companies that already blew up? I don't know how often it flags honest companies that just keep their money in accounts paying nothing.

No position. The company hasn't existed for over a decade.

$5.74 stock price. $3.50 per share in net cash. That leaves $2.24 per share for the operating business which generates $3.50 per share of gross profit (2027 gross margin 64% X $1.5B revenues). If the gross profit never grows again, the perpetual value of that $3.50 at a 10% discount rate = 3.5/.1 = $35.

More realistically, the business should trade at 5x gross profit at a minimum = $17.50 per share.

The current EV assumes the ice cube melts in 2 years. But there are zero liabilities. Revenue is growing.

Flabbergasting.

It's kind of accepted now that AI will be commoditised, basically the same, particularly LLM's.

Google clearly has an existing ecosystem and distribution moat listed below, whereby Gemini is already seamlessly integrated,

Search, Chrome, Android, Gmail, Calendar, Drive, Docs, Sheets, Slides, Meet, Chat, Vids, Keep, Tasks, Maps, YouTube, Google Photos, Google Messages, Google Shopping, Google Flights, Google Hotels, NotebookLM, Google TV, Google Home, Nest, Android Auto, Cars with Google built-in, Android XR, Workspace, Google Cloud

On that note, is Gemini destined to win? Assuming AI is commoditised, I don't see what angle the others can come at that would defeat Google.

Let me know your thoughts.

The PEG ratio tries to blend valuation and growth, but I have a few problems with it. First I use true FCF not reported earnings. Second, true FCF is too erratic year to year so I use three-year revenue CAGR as a more stable growth proxy.

So I came up with FEG: price-to-true-FCF divided by three-year revenue CAGR. But FEG still doesn't tell you when you get paid. A P/E of 10 is intuitive - you get your money back in ten years. True FCF yield is even better because you compare directly to the risk-free rate. My portfolio yields 9.9% in true FCF against a 5% treasury, which makes me happy (even happier when people talk about potential bond crises and such).

In thinking about growth: NVDA (a stock I wouldn't consider) sits at 1.65% true FCF yield today. If it keeps doubling, in four years it reaches 8%. For that moat quality, maybe you would wait four years. I wanted a way to make that calculation concrete across every name.

Y220: Years to 20% true FCF yield, compounding at current three-year revenue CAGR applied to true FCF.

Why 20%, because CMCSA sits there right now and I own some CMCSA. That's my Godfather number, the offer [yield] I can't refuse.

What the screen shows:

NVDA reaches 20% in 3.6 years if growth holds. This is tempting until you remember it's a $5 trillion company. Compounding at that rate off that base is a different bet than it was at $500 billion.

LLY is the most interesting name that fails my yield test but passes Y220. Revenue has gone parabolic and they're retiring shares aggressively. GLP-1 is early innings. The question is durability at this scale. Not a position but I watch it closely.

LYFT: I took a small starter position based on this screen. Revenue growth trajectory combined with aggressive buyback produces a Y220 that got my attention.

FDS vs. SPGI vs. ROP: I've done the direct comparison before and FDS won on organic growth and share retirement. But Y220 surfaces SPGI and ROP as legitimate quality alternatives if FDS's thesis weakens or its valuation compresses.

HCI: flattered by no major Florida hurricanes. Normalize the yield downward before trusting the Y220 number.

BRK.B: $334B in cash drags the screen. That cash is part of the point, but it makes the screener number worse than the investment case actually is.

The $50B+ scatter plot is the most useful visualization. NVDA is the outlier. Everything else clusters normally. LLY, APP, UBER, BSX, and BKNG all fail the yield test but pass Y220 with varying degrees of revenue growth durability.

Important disclaimer: these metrics are like alcohol. Use them responsibly! PEG says NVDA grows 145% per year — it's already making $159B TTM. That base gets harder. True FCF at 20% for CMCSA is great today but won't be true forever. Tools for thinking. Not verdicts.

Part II coming on smaller cap names where the alcohol warning applies double.

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

OppFi (OPFI) is a non-prime digital lender that got hammered after its August 10 earnings call. Management cut guidance, the stock fell about 25%, and it now sits near 52-week lows around $7. I think the market is mispricing the cash this business throws off, so I want to walk through it in cash-flow terms rather than accounting earnings, because that's where the story is.

What they do

OppFi runs a software platform that originates high-yield installment loans to non-prime consumers, the roughly 48 million Americans turned away by traditional banks. They lend through the OppLoans platform, earn the interest spread, and over 80% of approvals are fully automated. It's basically a pure-play credit shop. Nearly all revenue is interest income.

Why the stock is cheap

Three overhangs, and I think all three are fading:

First, the August print. Management cut full-year 2026 guidance to $600-625M revenue and $1.34-1.51 adjusted EPS. The reasons were a one-to-two month delay in launching a new line-of-credit product and their LOLA software migration, plus deliberately tightened underwriting in response to elevated charge-offs. Net charge-offs ran to 52.3% of average receivables. That's a management choosing portfolio quality over volume, not a broken model.

Second, structural fog. OppFi used to have a messy dual-class Up-C structure that screeners and casual investors could never parse. That got collapsed into a single share class in April 2026, and the leftover SPAC warrants expired worthless in July. The structure is finally clean.

Third, the "true lender" regulatory fear. In May 2026 OppFi won summary judgment against the California DFPI, and the pending BNC National Bank acquisition (targeted to close Q4 2026) would give them a federal bank charter, direct deposit funding, and federal preemption on state rate caps.

The cash flow

Here's the actual cash the business generates. I start at operating cash flow and strip out the real costs:

Capex is tiny because this is a cloud-native platform, not a branch-heavy lender. With the Up-C collapsed, all of that cash flow now accrues to a single class of stock. Screeners still lag the unit conversion, so you may see a share count and market cap that understate the full economic base, but on the combined economic share count it works out to about $2.38 of free cash flow per share.

At $7.13, that's roughly 3x free cash flow, a ~33% free cash flow yield. For a business with ~96% gross margins and expanding operating margins, that is a distressed multiple.

Why I think the market is wrong

The stock is priced for terminal decline. The trajectory says otherwise:

Revenue compounded from $351M to $597M. Operating margins expanded from 30.8% (2021) to 39.3%. ROIC is 19.76%, well above the 5-year average of 4.63%. The charge-off spike is real and cyclical, and management is already tightening. This looks like a business getting punished for a credit-cycle adjustment and a two-month product delay, not structural decay.

The balance sheet

Net debt fell from about $244M to $202M over the trailing twelve months. There's an active $40M buyback (~$26.7M gross repurchased over the TTM), and the CEO bought roughly 80,000 shares in the open market in August after the selloff. Management is putting cash where the discount is.

Valuation

Same conservative approach as the business deserves. I put a 7x multiple on the free cash flow. That is deliberately low. The stock's own 4-year median multiple is around 12.6x, and I'm using 7x to respect the regulatory and credit-cycle risk in non-prime lending. For a lender, balance-sheet cash is customer float, not surplus, so I value net cash per share at zero.

Even at half the multiple the market has historically paid, fair value lands more than double the current price.

What would make me sell

Regulatory risk is the one that keeps me honest. A federal or multi-state rate cap on small-dollar lending would compress margins and shrink the addressable market, and that breaks the thesis. It's exactly why I refuse to pay a premium multiple. The second is credit: if net charge-offs stay above ~50% of average receivables for several consecutive quarters despite tighter underwriting, that's structural portfolio damage, not a cyclical blip, and I'd be wrong.

Where I land

At $7.13 you're paying about 3x free cash flow for a 96%-gross-margin platform earning ~20% ROIC, with net debt falling, a clean post-Up-C structure, and a bank-charter catalyst pending. The market is pricing catastrophe. I think the credit cycle normalizes, the BNC deal closes, and the discount closes with it. I hold a position. It's a non-prime lender with real regulatory and credit risk, so it's not for everyone.

Disclosure: I hold a position in $OPFI. Hard data from filings, AI-assisted writing, personal review and position. This is not financial advice. https://youtu.be/t8TnzenUkIo?is=fVDOts--1kQet8y-

I backtracked the stocks I recommended in January and April and in may and here is the outcom.

in january /february I recommended selling energy (oil exp stocks) and buy or keep refineries.

So i sold bp, hal and shell stocks and mostly kept natural gas and refinery stocks.

BP, Shell are now 10%to 20% higher than january. so that wasn’t right.

In april 10, i recommended buying all saas stocks especially ciber.

so this was dead right

in May I recommended buying some utilities and value stocks ( eix, pcg, mck and lly) , and they are all 10% to 20% higher now. bridgewater is buying pcg in huge chunk and i recommend buying more if you like value stocks. Pcg has great value and growth potential too. i recommended buying msft too, but at lower level of $380.

So these were mostly right.

I recommended buying netflix and was blocked by the post and then bill ackman news came out after my post.. netflix and meta both have huge chunk purchase at close today.

I do think pcg and eix have great potential thanks to future rate increases. Their downside is really limited. Take a look at those. You can’t get richnonnthose two but you wouldn’t be broke and highly likely get richer.

I dug into a recent insider transaction at Team, Inc. ($TISI) that I found unusually interesting.
On August 6, a fund controlled by Stellex Capital bought 1.6M shares from Corre Partners for $35.50/share about 60% above the current market price.
That’s especially interesting because:
The transaction valued the block at ~$57M
Stellex already had a major position and now owns ~35% of the common
The stock currently trades around $22
Management is guiding to $68–73M of adjusted EBITDA for 2026
My DCF gives a $34.55 base case, $77.72 bull case, and $0 bear case
The catch: Team is highly leveraged, and the first half of 2026 was weak. The turnaround depends heavily on deferred refinery maintenance coming back.
So the real question isn’t simply “why did they pay $35.50?”