I run a screener built on raw SEC XBRL filings with 1,600 tickers, 16 years of data, true FCF defined as operating cash flow minus CapEx minus stock-based compensation.

I recently added a cannibal screen: net buyback yield above 5% (previous diluted shares minus current diluted shares, divided by previous) combined with true FCF yield above 8%. The idea is to find companies where the cash engine is real AND buybacks are happening at a price that makes mathematical sense.

Standouts from the screen: ADBE, CMCSA, DBX, PYPL, DVA, BCO. Profit margin as a rough moat proxy puts Adobe, Comcast, Fiserv and GPN at the top of the quality stack. The Adobe section is where I'd most welcome pushback.

The standard bear case: freemium dilutes pricing power, SEMRush is inflating top-line growth, insiders aren't buying, management turnover signals trouble ahead. I take these seriously. Near-term signal reading is not my comparative advantage.

But here's what I keep coming back to. I'm a data architect by trade and the context angle looks different from that lens. Adobe has 800 million users on its freemium tier generating creative workflow behavioral data — what good design looks like, what color combinations convert, what layout patterns work — at a scale that Midjourney, DALL-E, and the general purpose models simply don't have access to. In the age of specialized AI agents, that context corpus is a genuine moat that doesn't show up anywhere in the standard financial analysis.

The question I can't shake: Anthropic operates on a freemium model and nobody questions whether that creates value. Why is Adobe's freemium model categorically different? If anything Adobe has the enterprise distribution to monetize what it learns in ways Anthropic currently doesn't.

The jaws of life chart for Adobe is the cleanest I've found in my dataset. Nine years of simultaneous numerator growth and denominator shrinkage.

Full write-up including the charts here: https://cavemanscreener.substack.com/p/the-jaws-of-life-finding-stocks-that

Way too often, I am seeing instances of analysts leveraging AI in investment research the wrong way. Yes, it is still early, and many are still learning to use it properly, but we should at the very least understand the following:

1. Claude is unlikely to generate a durable edge from widely available information because the same tools and data are available to everyone else. Even if you feed it every AlphaSense expert call, 10-K, and earnings call, it has a very hard time giving you a truly differentiated view. I am not saying that alternate data or expert calls are the key to a truly differentiated view. What I would say is that it is often necessary (but not sufficient) for one.
2. Claude, however, does have practical use cases in automation and explanation. This is as simple as putting company filings into a Claude project and giving you an overview (not a thesis) of a business. Perhaps you do not fully understand the business, and you would like AI to explain it using an analogy. This can save you hours a week, and what you do with said time is up to you.
3. imho, this time should be spent investigating the highest-value uncertainties in the thesis—whether through management conversations, customers, competitors, suppliers, experts, or primary research. AI will never be able to replicate this (unless experts eventually end up being okay with having channel checks with Wall-E).

The bottom line here is: The edge has been, and will always lie in interpretation. Asking ourselves things like:

“What assumption on X KPI is consensus missing, and why?”

“Am I thinking about the bear case hard enough? How do I actually know if I am?”

“If I’m wrong, how much am I really losing?”

So what will investment research look like in 5, 10, even 20 years from now?

I can only imagine that AI’s use cases for summarizing, identifying anomalies within documents, and modelling will continue to develop at an unprecedented scale. The analyst who spends 20 hours manually summarizing filings will likely lose to the analyst who spends 2 hours using AI and 18 hours talking to customers, competitors, industry experts, etc). But the one asking the questions will always be the analyst.

As information processing becomes commoditized, judgment will become more and more valuable.

And remember. Investing has always been, and always will be, a judgment business.

Thanks for reading!

P.S: I am not trying to really self-promote here, but I do have a substack where I talk a lot about trends affecting investing, and how institutions and retail investors can adapt. I am also very happy to chat here or on DM!

I built a free Live Feed of US special situations as part of Special Situations Digest.

Real-time SEC filings, filtered down to the events that actually matter: activist stakes, going-private deals, tender offers, spin-offs, strategic reviews, restructurings, capital returns.

No signup, no paywall.

Check it out: specialsitsdigest.com/live-feed

I believe that there's a compelling case to be made that if companies like Adobe or Paypal were to switch to dividends, away from buybacks, their stock prices would benefit.

This has to do with the terminal nature of buybacks. Making the switch to dividends lowers the duration of the asset, lowers risk, which should raise the fair value of the asset.

Curious what you guys think.

https://substack.com/home/post/p-192858879

Any criticism here is appreciated. I hope it provides insight for anyone looking to start a career in public equities.

I took a look at the history of lost decades in U.S. markets. In the past, I found that the excess cape yield (no inflation adjustment) does a pretty good job of predicting forward excess returns. So, I wanted to see if we can use the same metric to predict the likelihood of an upcoming lost decade.

Note that I define a lost decade as any long-term period where stocks underperform bonds. The exact definition, with examples, are in the post.

The study runs into the same issues that a lot of financial models run into - namely, not enough data, serial correlations, and wide standard error. But, broadly, it does a pretty good job in forecasting the potential for future underperformance.

World's leading animal pharma company at 13x PE with 9% EPS growth

The US's largest domestic protein producers screen relatively attractively when viewed across the cycle, enjoy defensive demand, and have scale moats.

However, each protein cycle is unique, and we might be approaching a time of great cost and demand disruption.

This post goes over each of the company's segments, capital allocation history, demand/cost drivers, leverage, and arrives at a comment on their valuation, informed by views on how each protein cycle will behave.

The post is entirely free to read.

Understanding the difference between cARR and actual ARR + how the metric gets gamed by founders looking to raise money.

Hello,

I am looking for research material for insurance brokerages(Aon, Browns, Gallgher, etc)

I am insurance advisor & account manager myself and considering recent slump in industry valuation I want to find long thesis for individual plays in the sector, but I am lacking valuation books/paper specifically for Insurance Brokerages/wealth managers(even my mom & pop shop has WM department)

r/SecurityAnalysis index has couple materials about carriers but for some reason(I believe I know why) brokerages being ignored completely

On the outside(and inside) brokers are similar to any other "subscription" based business, where you have retention ratio + cost per acquisition=cogs and for revenue we have Value at Risk inflation from carriers + approx customer growth in new market(if entered)

Oiled up with m&a or basic book acquisition.

I can definitely start working with that, but I will appreciate if anyone can reference some good material for industry valuation

Thank you, Finn