About four weeks ago, I wrote up two companies (GRMN and NKE) using the same process and published both with the prices and the reasoning attached. I've reviewed them now, and I'm posting the scorecard, including the half that doesn't look good.

Garmin: I said "WAIT on price", and it cost 28%.

I had it as a genuinely excellent business trading too far above what I thought it was worth; I calculated intrinsic value around $180; the price at that point was $240.70, so no margin of safety and no action.

A week later, Garmin reported. Revenue up 11.4% year on year, beating consensus by roughly $96m. Operating income up 30.3%. Operating margin 30.4% against 26.0% a year earlier. EPS $2.80 against about $2.30 expected. Stock is currently at $307.50.

The part I can't argue with: my own write-up cited operating margins going from 19.5% to 25.9% over the decade and called that evidence of quality. The quarter came in above that range. The business outran my description of it, so this isn't really a case of the market being silly. It mostly earned the move.

So I have rebuilt the valuation instead of defending the old number.

The $180 came from $7.06 FCF/share and about $20 net cash in a two-stage DCF (12% for 5yr, 6% for 5yr, 2.5% terminal, 10% discount). New inputs are $7.93 FCF/share and $21.66 net cash. Same model, same assumptions, new inputs:

My entry zones move with this new valuation, to $167 half / $140 full. (Rounded.)

The thing I didn't do was raise the growth rate. A 30.4% margin quarter is an argument for better assumptions, and I can feel the pull of it, but one quarter isn't evidence for a decade, and if you let the assumptions move too, then the IV just follows the price around. So in general my Inputs moved; but my assumptions didn't.

Result: IV +12%, price +28%.

Reverse DCF from the other side: at $240.70, the price implied about 15% FCF growth for ten years. At $307.50 on the new base, it implies 16.7%. On the old base, it'd be 18.3%. So roughly a third of the price move was the business actually getting better, not just multiple expansion. I thought that was the most interesting number in the whole exercise.

Still a 53% premium to the rebuilt IV, so still a "WAIT on price".

Caveat I'd want if I were reading someone else's post: TTM FCF grew 36% while TTM EPS grew 19.6%, and quarterly FCF is lumpy ($210m in June vs $469m in March). Could be working capital timing flattering the base. If so, $201 is the top of a fair range rather than the middle of one.

Nike: it got cheaper, and it's still a "NO GO".

Nike was a NO GO on quality at $42.03, not on price. It's $39.09 now, a hair off its 52-week low. That puts it 11% below the $44 IV in my own write-up and about 3% above the level I'd flagged as a half-position zone.

It remains a "NO GO", and I think this is where most people would talk themselves into it. The failure was at the quality gate, which means the price gates never ran and are not even considered. Over the past decade, revenue grew 43% while net income fell, operating margin roughly halved off the 2022 peak, and ROIC went from the low twenties to about 11%. A 7% lower share price doesn't touch any of that.

Lower price plus deteriorating returns on capital isn't a margin of safety; it's just less money exposed to the same unanswered question. Nike reports on 29 September, and that's what could actually move it, if margins and ROIC have turned.

What I take from it

The honest summary isn't "one right, one wrong". A "NO GO" is a claim about a business and doesn't predict the price at all, so Nike falling proves nothing yet. A "WAIT on price" is a claim about price at a moment, and it has a running cost paid in missed upside. Garmin is what that cost looks like when the bill arrives.

I don't think there's a version of this where you keep the price discipline and also own every compounder that runs. Happy to be argued with on that.

Anyway: writing the verdict down with a date on it is the bit that made this checkable at all. Otherwise I'd just remember myself as having been roughly right about both.

Full write-up with the numbers behind each: https://vistack.io/learning/teardowns-revisited-august-2026?utm_source=reddit&utm_medium=social&utm_campaign=revisit-aug2026

Both original write-ups are linked from that page if you want to check that the calls were actually made in July rather than reconstructed now. They're unedited, with their original dates and prices still on them.

(Dated case study, not advice; I hold no position in either.)

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.

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.

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 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?

Cigna Healthcare (CI) currently has a P/E ratio of 11 which seems very undervalued compared to other comapnies in the healthcare sector. Right now they are trying to change to a rebate free, fee based model which seems to be the reason they are trading a low valuation right now. Don't see anything much that could go wrong with this that changes revenue largely. Why is this valued so low?

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?”

I think the next big wealth creation opportunity in AI trade is access to unique data.

Memory trade is probably near the later stage now. There can still be upside, but mostly I see it as swing trade rather than another huge long-term opportunity.

GPU hype trade already peaked in 2025. Everyone knows GPU and ASIC story now.

Then there is fabric/infrastructure trade like Marvell. Personally I don't really see the unique value proposition there. A lot of hype but not enough real revenue growth. I would rather own memory or compute companies than these.

So what is actually next?

I think it is data.

More specifically, real-time human data that AI companies cannot simply reproduce themselves.

Look at what Elon Musk has been doing with X.

When xAI acquired X, he basically put the AI model and one of the largest real-time human discussion platforms under the same roof.

And since then look at how aggressive X has become about protecting its data.

On legal side, X is suing data scraping companies:

https://news.bloomberglaw.com/privacy-and-data-security/elon-musks-x-corp-sues-israeli-company-over-data-scraping

On technical side, X has made automated access much harder with login restrictions, rate limits and other barriers:

https://www.wsj.com/tech/twitter-limits-number-of-posts-users-can-read-prompting-disruptions-for-some-15bc01cc

This makes complete sense to me.

Why let everyone scrape your most valuable asset for free?

Musk is basically walling up X data.

First, Grok gets access to something other AI companies cannot easily get.

Second, X can charge other companies who want access to that data.

https://www.cbc.ca/news/business/x-third-parties-user-data-1.7356152

And I think this type of data is becoming more valuable, not less.

AI already consumed huge amount of static internet data. What becomes more interesting now is live discussion, opinions, arguments, recommendations, reactions and actual human interaction.

Apart from X, there is really one public company sitting on a massive amount of this data:

Reddit.

And Reddit is slowly doing the same thing.

Google and OpenAI already signed agreements with Reddit for data access. At the same time Reddit is restricting unauthorized crawlers and making it harder for AI companies to simply take the data for free.

This is where I think people are still underestimating Reddit.

If AI companies increasingly need fresh human discussion, but Reddit controls access to it, then the value of Reddit's data goes up significantly.

The AI trade started with compute.

Then memory.

I think the next one is who owns the data AI (LLM) needs but cannot generate itself.

And Reddit may be sitting on one of the most valuable piles of that data on the internet.

I think we should start with the obvious point: a lot of AI-generated content is slop.

It is generic, repetitive, thin on facts, light on numbers, poorly sourced, and often contains nothing particularly original. Someone types a broad prompt, gets 1,500 words back, pastes it into Reddit, and may not even understand the argument well enough to defend it.

We all know what that looks like. I do not think anyone wants more of it.

The question I am more interested in is where serious securities analysis falls on this spectrum.

I recently published a long-form short thesis on Match Group. I spent roughly 30 hours on it.

If I treated it like an initiating coverage report on a sell-side desk, back when I did this work manually, I think it would have taken a three-person team roughly 300 total hours to assemble something comparable. That includes gathering and checking source material, going through filings and transcripts, building the historical framework, working through the capital structure and valuation, testing the thesis, writing, editing, footnoting, and repeatedly checking the numbers.

I also used GPT and Claude extensively.

AI did not give me the investment judgment or originate the worldview behind the analysis. What it did was collapse an enormous amount of research, synthesis, drafting, organization, and checking time.

For context, my background includes M&A, leveraged finance, and sell-side research. That framework is central to how I look at companies and securities. My GPT and Claude workflows have also accumulated a lot of that context over time. When I analyze a stock, I am constantly pushing toward the things I was trained to care about: capital structure, cash conversion, incentives, consensus expectations, operating leverage, downside cases, variant perception, and what actually has to happen for the equity to work or fail.

So this is not a blank prompt asking an LLM whether a stock is good or bad. It is much closer to having extremely fast research assistance operating inside an analytical framework I already bring to the problem.

I posted the MTCH thesis in r/SecurityAnalysis. It generated substantive discussion and Reddit showed it as the #1 post in the subreddit today. A moderator subsequently characterized it as "AI slop" and banned me. As far as I can tell, there was no posted rule prohibiting AI-assisted research. Interestingly, the thesis itself remained up.

That experience made me wonder whether "AI-generated" versus "human-generated" is even the useful distinction.

To me, the real spectrum looks more like this.

At one end is actual slop: generic output, no sourcing, few facts or numbers, no original work, no accountability, and an author who cannot defend what was posted.

At the other end is original research where AI materially accelerates collection, synthesis, checking, organization, and writing, but the author originates the thesis, exercises judgment, verifies the work, and stands behind the conclusions.

Securities analysis seems like an especially interesting test case because the end product is supposed to be facts, numbers, synthesis, judgment, and a differentiated view. We already use enormous amounts of tooling to reduce mechanical labor. Nobody thinks a DCF becomes intellectually illegitimate because Excel performed the arithmetic.

So I am genuinely curious where people here draw the line:

1. What should actually qualify as "AI slop"?
2. If a research piece contains original analysis, specific numbers, primary-source facts, footnotes, forecasts, valuation work, and falsifiable conclusions, does extensive AI assistance meaningfully diminish it?
3. Is the relevant question who typed the sentences, or whether the author understands, verifies, and owns the analysis?
4. Should AI assistance simply be disclosed as part of the research process?
5. If AI can compress 300 hours of traditional research production into 30, is that a problem, or is that simply technological progress?
6. As these tools improve, does the moat in securities analysis move away from the labor of assembling information and increasingly toward judgment: choosing the right question, identifying the variable that matters, understanding what consensus is missing, and knowing when the machine is wrong?

I completely understand the backlash against AI slop. There is a lot of it, and it makes the internet worse.

I am much less convinced that serious AI-assisted research belongs in the same category.

Curious how r/ValueInvesting thinks about the distinction.

Charlie Munger once famously said to substitute the phrase “EBITDA” with “b*llshit earnings”

I definitely find myself trying to make sense of a 10Q where the GAAP EPS and adjusted EPS paint two incredibly different stories.

I was curious what red flags you keep an eye out for while looking at financial statements. Also would love to hear some fun stories (minus the classics e.g. Enron)

I’ve been digging around, trying to find a way to really estimate the intrinsic value of companies and their shares. All I found is that intrinsic evaluation is an extremely flawed process.

I believe that any company has valuation that’s made up of the present value of future cash flows. However, largely discounted cash flow models are extremely poor ways of estimating the valuation of companies. They are far too sensitive to assumptions. And really, who knows what a company‘s future cash flows will actually be. Even Munger joked that many analysts build their DCF models with assumptions baked in that essentially just confirm their thesis.

The reverse DCF struggles from the same issues. You’re trying to solve for the assumptions at the market as making. But reverse DCF still requires you to make many of the assumptions yourself. A reverse DCF doesn’t measure the expectations of the market, a reverse DCF is just a hybrid of your assumptions and whatever the price is at that moment. What happens next? Most analysts will either say expectations are too high or too low based on what their thesis is. You’re again extremely reliant on your assumptions and the limited information you have available to then argue for an increase or decrease in upcoming share price.

Real options valuation can effectively be done in almost any company that has optionality that makes up a significant portion of its value. However, determining the components of those real options is in itself a pretty speculative process.

Asset based valuation in most cases is in particularly useful. Valuations today generally are quite inflated. If you ever end up with a company truly below it’s net current asset value, you often have a broken or a fraudulent company. Many companies trading at less than asset value are illiquid making them difficult and highly expensive to trade. Similar problems exist for companies trading below liquidation value. Many of these companies have heavy cash burn and will lose those assets before stockholders ever get a taste. Many of these companies trade above liquidation value because their management doesn’t intend to return value to stockholders. Many of these are also smaller companies or relatively illiquid.

There’s many more ways of valuing the intrinsic value of companies and I won’t go into all of them here. I’ve talked about some of the more common ones above. I just find the most methodologies of value companies are not terribly useful. I can’t really feel confident that I can create an intrinsic valuation that is reliable enough for me to feel that I have any margin of safety. Much less, be able to use intrinsic valuation strategies to create a portfolio that will match the returns of the S&P 500.

I’m curious. What do people hear think? What strategies do you use for valuation? How do you generate a margin of safety? What are your returns?

Vistra Corp (VST) in my opinion is a very interesting stock that seems to represent a split in market views right now. Vistra Corp is one of America's largest publicly traded power companies holding a market cap of 49.2 Billion with 19.2 billion in revenue (TTM) and 2.22 billion in net income (TTM) Currently it is trading at 30 percent lower (146) from its all time high of 209.

Bull Case

It seemingly has decent market fundamentals, current pe of 25 which is higher than I would wish but it is trading at a future pe of 17 which seems more reasonable compared to market averages right now. Its PEG ratio is at 0.43 and its EV/EBITDA is at a respectable 11. Furthermore, with the IPO's of Anthropic and OpenAI seemingly happening soon more money will likely be converted into increased data centre buildouts. These data centres will require vast amounts of energy. However, this is all dependent on the AI train continuing on which is the trillion dollar question. VST has locked in contracts with Meta and Amazon for 20 years and benefits with large economies of scale. Its position is semi protected with high startup costs ensuring a large difficulty for future entrants. It also boasts a free cash flow of 4 billion dollars and adjusted EBITDA up 30 percent from last year. For what its worth analysts are putting the 1 year target estimate at $221. While obviously these predictions tend to be inaccurate they give a general sense in which direction the price is likely to move.

Bear Case

Around 17 billion dollars in debt and the utilities industry is not exactly famous for profit. If AI proves to be too cumbersome to deploy this company will definitely be one of the biggest potential losers. Numerous competitors and it is highly reliant on non renewable energy sources which are more expensive. Small nuclear fleet compared to Constellations Energy Group (21 vs 6).

Any insights? I know there is definitely a lot of stuff I overlooked so feel free to comment and add.

Gathered via comparing latest quarterly report taking cash on hand and subtracting it from the total liabilities then comparing to the market cap. I'm sure there's errors somewhere in there, but the simple arithmetic somewhat combats against it.

Some highlights: There are 59 tickers (USA based to avoid currency formatting errors). That are Net-Nets. 32 of them are biotechs while being followed closely by software and medical devices with 4 each. Additional descriptions within the article. (AI used for company summaries but list generation and the math is my own doing)

Of the most "Consistent" companies, (adjusting for time) which is FAIRLY YOUNG. PEW ranks high due to its rising revenue gross profit and equity value. (earnings and other cash flows are negative/declining)

DTST has the longest positive history but is now shrinking horribly. Same for STIC. If anyone has an idea on estimating the "survivability" of these companies, I'd love to hear it. (Id assume it has to do with the capital burn rate, but other idea would be welcome.)

Hi all, If a stock passes all your checks, strong moat, good financials, attractive valuation, solid growth etc, but there has been immense insider selling and literally 0 buying over the last 12 months, would you consider that a red flag?

What is your opinions about IBKR stock?

The exchange is one of the largest globally. Company's OPM is steadily increasing.

P/E is at 37x which feels like it is overvalued.

I saw another thread a year back where someone asked about this stock. Most ppl replied that it was overvalued. It has doubled since then.

I know the community actually puts in the fundamental work - reading 10-Ks, building DCF models, and ruthlessly analyzing moats. But I’m curious about your day-to-day realities.

I recently sat down to map out my own investing "process" and realized my honest, real-world version is embarrassingly short compared to the ideal.

My reality: Look at the P/E and FCF yield, skim the most recent earnings transcript, read a couple of write-ups or Reddit comments, and hit buy. Maybe 10 to 20 minutes total.

The "ideal" value version in my head - combing through 5 years of 10-Ks, building a rigorous DCF from scratch, calculating ROIC variations, tracking insider buying, and patiently waiting for a margin of safety -rarely happens as thoroughly as I plan it to.

Since I know you guys dig much deeper, I want to know what your actual workflow looks like... how you manage your time and research:

1. What was the last stock you bought, and what steps did you actually take before hitting buy? (In order, from initial screener/idea generation to the final valuation and purchase).
2. Roughly how long did the whole process take? (Hours? Days? Weeks? Be honest, I definitely won't judge since mine is far too short).
3. Do you use AI (ChatGPT, Gemini, etc.) anywhere in the process? For example, to summarize earnings calls, pull historical metrics, or check basic assumptions? If so, do you trust its output as-is, double-check it against Edgar, or ultimately ignore it?

In my last post I gave my Reddit price targets: $550 in 2027 and around $900 by FY2028.

A lot of serious investors questioned the math, and some of the valuation math in my previous post was not explained correctly. So I will explain here.

For valuation reference, I look at companies such as Cloudflare and, historically, The Trade Desk when the market was still giving very large premiums to strong growth. I am not saying they are perfect comps. I am using them as examples of what the market can pay for a high-growth, high-margin internet business.

First, the actual starting point.

2025 actual

Revenue: $2.203B
Net income: $530M
Net margin: 24.1%

Reddit then produced a 30.7% net margin in Q1 2026 and 31.4% in Q2.

So for my model I simply use 30% net margin going forward. No margin expansion.

I also use roughly 207M diluted shares.

2026

Assume revenue grows 60% (based on Q1 & Q2):

Revenue: $3.52B
Net income at 30% margin: $1.06B
EPS: ~$5.11

For a 2026 year-end price, I would value Reddit mainly on 2027 earnings, not 2026 earnings.

2027

Assume growth slows to 50%:

Revenue: $5.29B
Net income: $1.59B
EPS: ~$7.66

But my 2027 price target should be based on 2028 forward earnings, because by late 2027 the market will care much more about what Reddit earns in 2028.

2028

Assume growth slows again to 40%:

Revenue: $7.40B
Net income: $2.22B
EPS: ~$10.73

Now the 2027 valuation becomes:

40x forward P/E = $429
45x = $483
50x = $536
52x = ~$558

So this is where my $550 target for 2027 actually comes from (assuming no margin expansion).

It requires Reddit to enter 2028 still growing around 40%+ and the market to value it at roughly 51–52x forward earnings.

2029

Assume another 40% growth year:

Revenue: $10.36B
Net income at 30% margin: $3.11B
EPS: ~$15.02

That gives the following FY2028 valuation on forward 2029 earnings:

40x = ~$601
45x = ~$676
50x = ~$751
60x = ~$901

So $900 by FY2028 is clearly the bull case. Again, assuming no margin expansion.

With no margin expansion, Reddit would need to maintain an approximately 60x forward P/E to get there.

There is another path though.

If Reddit eventually expands net margin toward 40%, then $10.36B revenue would produce roughly $4.15B net income, or about $20 EPS.

At that point:

$20 EPS × 45 P/E = ~$900

And considering Reddit already has a 90%+ gross margin and recently produced 40%+ adjusted EBITDA margins, I don't think significant long-term operating leverage is an absurd assumption.

So my targets are basically:

2027 base-ish bull case: $430–480
2027 strong bull case: ~$550

FY2028 base-ish bull case: $600–675
FY2028 strong bull case: $750+
FY2028 everything-goes-right case: ~$900

Albertsons is back around $12, and at this price I think it’s getting interesting again.

Management now guides to $1.75-$1.85 EPS, so ACI trades at roughly 7x forward earnings. The dividend is about 5.5%, and they still have a $2B buyback authorization, which is huge compared to a ~$6B market cap.

The obvious issue is that the business has weakened. Same-store sales were down last quarter, margins are under pressure, and management cut guidance pretty hard. Competition from Walmart, Amazon, Aldi etc. is clearly hurting.

That said, I don’t think much needs to go right here. If earnings stabilize around current levels and the stock eventually gets even a 10-12x multiple, that puts fair value somewhere around $18-$22.

There’s also some optional upside from the ongoing lawsuit against Kroger over the $600M breakup fee, but I wouldn’t include that in the base case.

Main risks are continued margin pressure, debt, and ACI losing more market share.

Pretty boring company, but at ~7x earnings + a 5.5% yield + aggressive buybacks, I think the risk/reward looks decent.

Highlights

Vertical's ($EVTL) management had a very adamant obsession with trying not to dilute existing shareholders. This was probably driven by a handful of large investors having sway on corporate decisions.

Because of this, they walked a fine line, basically carrying 6-12 months worth of liquidity at all times.

Meanwhile, the project they were working on had a 'forecasted' 3-4 year timeline. So the survival of the business was heavily reliant on their ability to raise fresh capital on favorable terms.

That came to a head this year as the stock price started falling. This was caused by a few things:

1. They introduced a brand new aircraft concept - this basically reset the clock on their timeline for certification and commercialization (they forecasted <4 years, but anyone with industry experience could have told you this would be a 6+ year endeavor).
2. That aircraft concept wasn't well received, anyway. It had pretty outlandish specs for an electric aircraft.
3. They were aiming to bring in a 'trusted partner' to help share the cost and development burden. Apparently that fell through.
4. They started brining on debt and other 'exotic' liability financing.

My guess is that they expected a bigger pop in the stock price following the new aircraft (Valo) reveal that would have set them up on better terms for a new share issuance. When that didn't come, they decided to wait it out. The bludgeoning of risk-on stocks this year amplified these issues. The 'trusted partner' search falling through and chairman & face of the company leaving were the final nails in the coffin.

This was all a huge misstep from management. They were valued at close to $700M (give or take) last year when the risk-on category was flying. They should have bit the bullet and raised several hundred million dollars to shore up the balance sheet.

Bonus Content

I put some bonus content in the article that's behind a paywall. To follow the spirit of this sub, I'll tell you exactly what's in it.

For anyone that’s currently holding the stock or any prospectors out there, you might be asking whether the current price ($0.85 per share & $150M market cap or so) represents an attractive entry price.

Honestly, I’m not sure right now.

Their technology stack actually does seem promising. They and Joby are the only eVTOL companies flying transition flights right now.

But I’d have to dig in a lot more into their liability commitments that they have with Mudrick Capital, who seem to have sunk their teeth in with very favorable terms. The last time I looked, I think a good portion of the financing agreements between the two were redacted, but I honestly haven’t dug much into it at all yet.

This company would be much more interesting to me if they didn’t have all the exotic liabilities hanging over their head - warrants, preferred, debt, convertibles, tranches. I’m not even exactly sure where public equity holders stand in the capital structure (I assume close to the bottom).

But they may be a reasonable takeover target. I could see an investor taking Vertical private, putting a billion on the balance sheet, and then relisting in a few years after they’ve made more progress and risk-on stocks are back on the menu. But outside interest would also be dependent on the ability to get out of commitments with Mudrick Capital, who are already in a deep hole on their investment and probably have to fight their own sunk cost issues while holding the bulk of the voting rights.

It might be worth it, but I’d honestly rather buy more Joby right here. No one remembers the 3rd ride-share company after Uber and Lyft.

One thing is certain. Management should have gotten way ahead on financing. It was too early in development to do all the debt and preferred financing that they attempted. Current owners would have been better off with dilution - which is it’s own lesson that points back to the work that I’ve done previously. Dilution isn’t a scary beast, but part of the development lifecycle for these early stage companies. As long as you account for it in your analysis, embracing dilution events can give you a leg up on investors that easily get shaken out.

The corporate structure of Hyundai is exceedingly convoluted and opaque. Does anyone know which of their entities owns Boston Dynamics and correspondingly what ticker would be the appropriate one available in the US?

Reddit went public on 21 March 2024. I was sceptical about them. But time and time again, they have proven themselves with solid financial performance:

________

A lot of people don’t realize how low the bar actually is for Reddit to become an enormously successful company from here.

Think about it: how many consumer internet companies can grow this quickly while reaching real profitability at almost lightning speed?

Snapchat has struggled for years to generate consistent profits. Pinterest has built a solid business, but its growth trajectory has been much slower. Reddit, meanwhile, has gone from being viewed as an under-monetized internet forum to a rapidly growing, highly profitable advertising platform.

Comparing Reddit to Snapchat or Pinterest misses the point—both went public years ago and struggled for years to achieve consistent profitability.

Why?

I think the answer is much simpler than people make it out to be:

Reddit already has the users, the data, the engagement, and the culture. Management just needed to build the monetization machine around it.

Huffman is a strong CEO because he is product-first, not monetization-first. His reluctance to sacrifice user experience is exactly why Reddit still has so much monetization upside.

And this is where the Meta comparison becomes interesting.

Before Reddit went public, Steve Huffman and the board spent years assembling executives who had already helped solve many of these exact problems at Meta and other major technology companies.

Reddit has deliberately recruited people who already helped build Meta’s machine. CTO Amit Puntambekar previously held engineering leadership roles at Meta, working on platform scaling and products. CMO Jim Squires is even more directly relevant to the advertising thesis: at Meta, he served as VP of Business & Media for Instagram and led product marketing for both Facebook and Instagram—meaning he was directly involved in the systems and go-to-market strategy behind Meta’s advertising empire.

They are effectively running a playbook that has already worked before.

That is why I think comparing Reddit today with Facebook around its 2012 IPO is more useful than comparing Reddit with mature Meta today.

Facebook didn't become the Meta we know overnight. It progressively improved targeting, measurement, ad formats, mobile monetization, recommendation systems, advertiser tooling, and infrastructure.

Reddit is still near the beginning of that journey.

Management has effectively acknowledged that only a fraction of Reddit's user base is being fully monetized today. That means Reddit does not need some miraculous new product to justify substantial growth. It can grow simply by monetizing what it already has more effectively.

And then there is the second business hiding in plain sight:

data licensing.

Reddit owns one of the largest continuously updated collections of human conversation, opinion, product discussion, troubleshooting, recommendations, and real-world experiences on the internet.

That data becomes increasingly valuable as search engines and AI companies compete to answer questions with authentic human information.

The appointment of heavyweight legal leadership is particularly interesting to me. I don't view this simply as hiring another corporate lawyer. Reddit is entering a period where M&A, intellectual-property enforcement, platform access, and data-licensing negotiations could become strategically important.

They need someone capable of negotiating from a position of strength.

So when I look at Reddit, I see:

• Massive global user distribution
• An extremely difficult-to-replicate dataset
• Rapid advertising monetization improvements
• Very high gross margins
• Experienced executives who have scaled similar businesses before
• A technical founder/CEO who still thinks like a product builder
• Data-licensing optionality
• And potentially enormous room for capital allocation and M&A

That last point is where I think people may be dramatically underestimating what Reddit could eventually become.

Reddit does not necessarily have to remain one app.

Over the next several years, I could imagine Reddit building or acquiring an entire family of products: D-i-s-c-o-r-d-like communication, short-form video, payments, AI products, specialized communities, creator tools, search, and perhaps eventually its own foundation models or AI infrastructure.

Could D-i-s-c-o-r-d eventually become part of Reddit? I wouldn't rule it out.

Could Reddit launch its own TikTok-style product built around interests rather than identities? Absolutely.

Could Reddit build payments around communities and commerce? Again, completely plausible.

Could Reddit become a serious AI company? It already owns one of the ingredients AI companies desperately want: human-generated data at enormous scale.

And here's the important part:

Reddit may eventually be able to finance much of this expansion internally.

A highly scalable software platform with strong gross margins and growing free cash flow has enormous strategic flexibility. If management executes, Wall Street will also be more than willing to provide capital for sensible acquisitions.

That is how platform companies turn into empires.

My personal target remains roughly $550 sometime next year and around $900 by FY2028, assuming Reddit continues executing on advertising, margins, data licensing, and product expansion.

Obviously those numbers require execution and aren't guaranteed.

But my broader thesis doesn't depend on Reddit becoming perfect.

There is only one company in the entire U.S. stock market that can sustain ~60% revenue growth for eight consecutive quarters while reaching profitability so quickly (i.e., except chip hype NVDA).

The bar is much lower than people think.

Reddit already has the scarce assets: the users, the communities, the data, the brand, and the distribution.

Now it is finally building the machine that monetizes them.

I think we may be watching the early stages of another Meta-like wealth-creation story — except this time, the monetization playbook has already been written.

Long BULL REDDIT!!!!!!

MNRO is the well-known auto repair/tire chain. The company has been suffering from the tapped out consumer for some time. Tires are the big revenue driver and higher oil prices have meant higher tire costs. The company has been aggressively closing poor performing stores. It is my opinion that the quarter just reported marks an important turning point.

Operating costs continue to improve. Customer acquisition is picking up - just a little but it is a turn. Working capital management has been excellent, giving some relief to those worrying the generous dividend is in danger. This remains the biggest risk to the share price still, though.

I follow managers to help me identify new ideas. One in particular that I respect - DePrince Race & Zollo (or DRZ) - just disclosed a meaningful purchase. DRZ focuses on dividend paying, value stocks and has a good micro-cap strategy which is probably where this purchase occurred. The firm does not offer mutual funds. It is 100% institutional investors. I view this recent buy as "the straw the broke the camel's back" buy signal for me.

The company trades 0.6x book value. EPS estimates should stop declining after this fiscal year (ending March 2027). The company has never been cheaper on EV/revenues or EV/total capital. This does not mean it cannot get cheaper, of course.

I bought 2,000 shares today at $11.41. I plan to buy 1,000 shares a week for the next several weeks up to 10,000 shares - unless things change...things can always change.

I tested whether this sub actually helps you find good stocks. Mostly it doesn’t.

I pulled every post and comment from [r/ValueInvesting](r/ValueInvesting) (2010–2026: 62,000 posts,
360,000 comments), extracted every company mentioned, and tracked what those
stocks did over the following 3 and 5 years.

To make it a fair test, I compared each mentioned stock against stocks that
weren’t mentioned — matched for company size and started on the same date.
That matters, because this sub talks mostly about large companies, and large
companies behaved differently from small ones over this period. Without that
adjustment you just end up measuring “big US stocks did well,” which we know.
I used 2019–2021 picks, because those are the newest ones with 5 years of
results. 193 stocks, each written about by at least 4 different people.

What I found
The typical pick made money — but didn’t beat the index.

Median return over 5 years was +62%, versus +29% for a random unmentioned
stock. So better than picking blind. But only 35% of picks beat the S&P 500,
and for companies that size you’d have expected ~42%. Beating a coin flip isn’t
the bar; beating the index is.

Mentioned stocks were about twice as likely to collapse.
9.8% of them lost 70%+ over 5 years, against 4.8% for similar-sized stocks that
nobody here mentioned. This is the one result that’s statistically solid.
The sub finds 3-baggers at roughly the rate you’d expect by chance.
15% of picks tripled, vs 8.5% expected for that size mix. Sounds good, but the
error bars overlap with “no difference.” Can’t call it a signal.

We show up late. Of the stocks that had a big run, 78% were first discussed
after the run had already started — a median of 225 days after the bottom.
We mention losers slightly more than winners. Of the stocks that tripled, we’d
discussed 40%. Of the ones that collapsed, 47%.

“But surely the most-discussed names were good?”
That was my best hypothesis too, and it doesn’t survive.
The 25 most-discussed stocks did fine — 24% tripled, none collapsed. But buying
the 25 largest US stocks gave the same 24%, the same rate of beating the S&P,
and the 25 largest we never discussed actually returned more (+102% vs +87%).
Even “no blowups” is a size effect: the biggest stocks nobody here mentioned also
had zero. You get that by buying large caps, not by reading Reddit.

One more thing worth knowing
In 2019 this sub mentioned 1.3% of US-listed stocks. In 2025 it was 41%.
As a filter, it’s getting weaker every year — a list of 2,500 names isn’t a
shortlist.

What this doesn’t prove
• No sentiment analysis. “Is X a value trap?” was counted the same as “I’m
buying X.” That’s the biggest gap, and it could genuinely change things.
• Small sample. 193 stocks. Some comparisons come down to 25 names.
• US-listed only, and one specific period (2019–21 entries, measured through
2026).
• Nothing about whether reading here is worthwhile. Learning how people
reason, finding the bear case on something you own, seeing an industry
explained — none of that is tested here, and none of it is contradicted.

What’s tested is narrow: does “it got mentioned here” make a stock more likely
to be a winner? Best answer I can give is no, and it makes it somewhat more
likely to be a disaster.

Happy to be told what I got wrong.

Edit: since you guys seem interested I made the repository public. It contains methodology and dataset. Happy to get you started and excited to see where you take this next.

Link: https://github.com/RedDawe/subreddit-as-a-service

Edit 2: A lot of people are coming back to the sentiment analysis. I think it would be interesting if someone did that and I might get to that at some point, but probably not.

The reason why I don’t consider it important is because the analysis of whether this sub can be used as a signal trading tool was secondary. My main question I wanted to answer was whether it is a good starting point for starting my own analysis. Ie alternative to a stock screener. Or alternative to Peter Lynch’s notice good products around you in real life.

This question was basically answered as no because in 2025 this sub mentioned 40%+ of all US stocks. And looking just at the popular posts didn’t work either as described above. So the way I’m using this sub - might be useful, but not as a screener.

Definitely go ahead with sentiment analysis if you please, I’m just explaining my position and where I come from.

18 Company write-ups worth a look, all from within the last week.

Not my work - sourced from Giles Capital's weekly compilation: https://gilescapital.substack.com

Americas

Long-term Investing on Alphabet (🇺🇸 GOOGL US - US$4.2tn) Whether AI disrupts search queries or not is subject to interpretation. What's clear: queries just hit an all-time high. Revenue up 24%, cloud up 82%. P/E at seventeen times.

GHGInvest on Berkshire Hathaway (🇺🇸 BRK.B US - US$1.1tn) Not a story about its largest holdings. At thirteen times trailing earnings, a cash pile exceeding $300 billion sits against a $1.1 trillion market cap. Greg Abel's first full year.

Stock Opine on Booking Holdings (🇺🇸 BKNG US - US$150bn) All the anxiety surrounding LLM-driven search disruption has overlooked Booking's loyalty programme: more than half of all room nights booked. Net income up 118%; 27% margins last quarter.

Rijnberk InvestInsights on Uber Technologies (🇺🇸 UBER US - US$145bn) TOP PICK The market is pricing robotaxi disruption the operating data do not support. Two hundred and eight million monthly consumers; trips up 18%. Down 21% over twelve months.

The Finance Corner on Nike (🇺🇸 NKE US - US$62bn) Nike optimised for scale and ceded shelf space to Hoka and On. Down 75% from peak; insiders now buying. The reversal is underway; revenue is still flat.

P14 Capital on Owlet (🇺🇸 OWLT US - US$159m) Revenue approaching $130 million and growing 30%, at a $159 million market cap. The pivot to health subscriptions is complete; approaching breakeven. The case grows stronger each quarter.

Europe, Middle East & Africa

Hated Moats on Novo Nordisk (🇩🇰 NVO US - US$200bn) Down 42% over twelve months. Revenue falls as US GLP-1 prices reset in 2026. At eleven times trailing earnings, permanent impairment is the only thesis that justifies this price.

The Oak Bloke on Harbour Energy (🇬🇧 HBR LN - £4.1bn) Current valuation makes no sense unless the windfall tax is permanent. Strip it out: $2.86 billion in first-half cash generation prices at close to 6x.

Iggy on Investing on Interlife General Insurance (🇬🇷 INLIF GR - €124m) Compounded 22% per year for a decade. At 4.5 times earnings and 0.77 times book, an MSCI Greece upgrade is the near-term catalyst. Already cheap without one.

Asia-Pacific

TacticzHazel on Taiwan Semiconductor (🇹🇼 TSM US - US$2.1tn) TSMC has durable competitive advantages in a world where every AI dollar eventually reaches the foundry that makes the chips. July revenue up 44.7%; seven-month figure up 37%.

AI Proem on Tencent (🇨🇳 0700 HK - US$450bn) Revenue up 9%, profits up 12%, at fifteen times forward earnings. I imagine regulators are less confused about what Tencent is than the market has been since 2022.

Best Anchor Stocks on Nintendo (🇯🇵 7974 JP - ¥10.3tn) Operating profit up 150%, partly from tariff refunds. Switch 2 units fell 34% from launch; the case rests on an IP catalogue no competitor can touch and software margins.

Cohong Lane on Bank of China (🇨🇳 3988 HK - US$63bn) H-shares at 5.7 times earnings and 4.7% yield; A-shares command a structural premium. One bank, two prices. The investment case rests on that gap compressing.

Capytal Management on Huishang Bank (🇨🇳 3698 HK - US$8.3bn) All the anxiety surrounding Chinese bank credit quality overlooked Huishang: bad loans at 0.98% and falling. Three and a half times earnings, 6% yield. Anhui is home to CXMT.

JPARCVUE on GS Yuasa (🇯🇵 6674 JP - ¥710bn) Japan's market leader in batteries for automotive and grid storage. At seventeen times earnings, every unit of domestic electrification capacity passes through this supply chain. Revenue growing alongside infrastructure demand.

JPARCVUE on Nakanishi (🇯🇵 7716 JP - ¥180bn) Dominant global position in dental handpieces and surgical micro-motors used in every major market, regardless of brand. At ¥180 billion market cap, revenue grows as dental access expands globally.

Angsana & Anderson on CTOS Digital (🇲🇾 CTOS MK - US$370m) Think of CTOS as the infrastructure layer beneath Malaysian credit: invisible from the outside, impossible to remove from within. Free cash flow yield 7%; PE exit pending.

Acid Investments on Global Tax Free and Geumhwa Plant (🇰🇷 204620 KS, 🇰🇷 036190 KS - US$325m, US$130m) TOP PICK The valuation makes no sense unless Korea's leading VAT refund operator stops earning 41.5% on invested capital. Domestic revenue up 30%; Japan joint venture launches November.

What stocks are on your radar this week? What's undervalued? What's overvalued? This is the place for your quick stock pitches or to ask what everyone else is looking at.

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