How do you, how do you genuinely stop guessing
in the stock market? Pete, I mean, what if you
could take a simple idea, a hypothesis, use AI
tools, you know, consumer level stuff, and find
companies that did way better than the S &P 500,
like more than double? That is exactly the puzzle.
Yeah. That's what we're digging into today. We're
looking at a specific framework that historically,
anyway. generated 22 .4 % returns. Compare that
to the S &P 500's 9%. And that difference, that
edge, it came from using tools you probably have
opened right now. like on your desktop. So welcome,
everyone, to this deep dive. We're focusing on
these AI -driven investing frameworks. Our mission,
really, is to cut through the hype and show you
exactly how tools like Chat GPT and Google Sheets,
things most people can access, can actually run
some serious systematic research. Right. We'll
start with the core concept. First, this NFL
strategy. Then we'll get into the nitty gritty,
the data filtering, the scoring, and then the
really practical bit. We're giving you these
deep multi -layered prompts you can take and
use right away for your own research. Okay, let's
start with the hypothesis itself. NEFL. The big
idea seems pretty straightforward. Combine strong
business protection with dedicated management.
So network effects plus founder -led companies.
That's NEFL. Exactly. And the network effect
part, that's what builds the moat, the economic
protection. It's that simple idea. the product
gets better, more valuable as more people use
it. That distinction is really key, isn't it?
Like, Visa or MasterCard, every time a new store
accepts the card, it's better for everyone who
has the card. Mm -hmm, and vice versa. It's this
loop, this self -reinforcing thing that makes
it incredibly tough for someone new to just jump
in and compete at the same scale. Yeah, it builds
this huge wall around the business. Yeah. Protects
that market position potentially for, you know,
decades. That's the kind of structural advantage
you're looking for. And the second piece, founder
led. That anchors the vision, you said. Why does
who's running the show matter so much here? Well,
founders just have more skin in the game, right?
They're not just a hired CEO maybe looking at
the next quarter's bonus. They actually built
the company. They often own a big chunk of it.
So they're naturally thinking about the 10 year,
the 20 year future, not just hitting the next
earnings target. You think of someone like maybe
Mark Zuckerberg at Metta. Still driving that
long term vision, even when the market gets noisy
in the short term. Exactly. So the NFL idea is
combine that structural defense, the network
effect, with that kind of dedicated long view
leadership. combo should theoretically lead to
companies that consistently outperform. OK, but
if these companies have such obvious strengths,
these moats and dedicated founders, why doesn't
the market just price that in immediately? Why
aren't they always expensive? Well, because the
market often misses the nuance. It gets caught
up in short -term stuff. So that inefficiency,
that's the opportunity. Precisely. And that's
where AI becomes this massive time saver. Which
brings us to step one, the initial screening.
Right. Instead of spending weeks, maybe months,
reading through hundreds of company reports manually,
we used a single prompt. Just natural language.
Yeah. We basically asked the AI, find me all
the S &P 500 companies that have both network
effects, A &D, or still founder -led. Simple
as that. And boom, it came back with 26 companies
instantly. That speed, that efficiency gain,
that's the first big win. Yeah. And I still wrestle
with prompt drift myself sometimes, getting the
AI to stay on track. But for just raw data gathering,
pulling lists based on clear criteria, AI is
just fantastic. It saves so much time. Totally.
So then we set up the back test scenario. OK,
what if we put $100 into each of those 26 companies
right when they went public on their IPO date?
Total imaginary investment. $600. And then you
compare that portfolio's performance against
the S &P 500 index over the exact same time frame.
It's crucial to note, though, the results are
all over the place for individual stocks, right?
Like Netflix was astronomical. $100 turned into,
what, over $100 ,000? Yeah, insane. But then
you had others, like Snap, where the $100 actually
lost value. And that variance that really highlights
why you need more filtering. You need to go deeper,
which is exactly what the broad market often
fails to do. So how critical was nailing down
that exact historical IPO date data? Was that
tough to get right for the backtest accuracy?
Oh, absolutely. Getting that correct historical
data is key. Otherwise, the comparison isn't
fair. OK, so that brings us to the maybe the
most interesting part. Step two. Adding nuance
with this 1 to 10 scoring system, because like
you said, the real world isn't just yes or no
for something like network effect. Exactly. That's
a really powerful point. We had to figure out,
OK, which companies have a kind of weak, maybe
questionable network effect, let's say a 3 or
4, versus those with a truly dominant, almost
unbreakable one, a 9 or 10. And asking for a
score, not just a yes or no, that helps with
accuracy too, right? Less risk of the AI just
making something up. That's the thinking, yeah.
when you forced it into a binary, yes, no, the
risk of hallucination, as they call it, seems
higher. It might give a confident yes based on
weak evidence, but asking for a score like one
to 10, it forces the AI to weigh different factors
to give a more measured judgment. We found that
dramatically increased the reliability. And the
tool for this wasn't some super expensive finance
terminal. It was just Google Sheets add -ons.
Pretty much, yeah. Free add -ons that let you
run AI prompts right there in the spreadsheet
cells. Super accessible. OK, so what did the
prompt look like? It was basically a formula.
Tell me on a scale of 1 to 10 how much company
name benefits from network effects. 1 is not
at all. 10 is very strong network effects. Simple.
Whoa. I mean, just imagine scaling that, running
that kind of analysis across all 500 SMT companies
yourself would take forever. But doing it with
these tools? That's incredible power for an individual
investor. Yeah, it took about 30 minutes total.
Yeah. And gave us these detailed ratings for
every single S &P 500 company that costs with
maybe 15 bucks in API credits. It's just insane
data efficiency. But hang on, how did you check
those scores? How do you know the AI wasn't just
guessing a number? How do you validate that 1
to 10 data point before you relied on it? Ah,
good question. That's always the challenge with
generative AI and finance, right? Validation.
We did it by spot -checking the extremes. We
looked at the companies the AI scored highest,
like Visa, Adobe, the obvious ones, and checked
that against, you know, consensus expert opinions,
financial reports. Okay. And then we checked
the low -scoring ones, too. We found the AI was
generally pretty good at applying the definitions
we gave it in the prompt. It wasn't perfect,
but the scores directionally made sense. Gotcha.
Okay, that makes sense. So once you had that
reliable scoring, what about steps three and
four, building the portfolio tracker app? Was
that coding? Nope, no coding required. That part
was mostly about visualizing the data and being
able to filter it easily. We used simple prompts
and a no -code tool to build a basic web app.
It took maybe, I don't know, 45 minutes of tweaking
with just text commands to get the interface
working how we wanted. OK, so you've got the
score database. You've got the app to filter
it. What was the final cutoff? What did you filter
for to get the highest conviction ideas? We went
for the absolute strongest candidates based on
the hypothesis. So network effects score between
8 and 10, and founder -led score between 8 and
10. really stringent, and only six companies
actually met both of those high bars. Only six
out of the original 26. Out of the whole S &P
500, yeah. Well, out of the 26 initially identified,
then scored against the whole 500s of a very
narrow filter. Right, and the result of focusing
just on that top tier, that highest quality slice?
That specific, highly selective portfolio. It
returned 22 .4 % over the back test period, compared
to, remember, 9 % for the S &P 500 overall. The
key takeaway wasn't just use AI. It was using
AI to apply this deep nuance, this scoring, to
a simple, clear, testable idea. That NEFL example
really shows the power of thinking systematically.
Okay, let's pivot now. Let's talk about how listeners
can apply this kind of thinking. Let's share
some of those deeper prompts you mentioned, the
ones for customizing your own research. Absolutely.
Because the real value here isn't just copying
the NFL strategy. It's about defining your own
investment goals and then using AI. to handle
the complex filtering and constraints. So yeah,
we've got three pretty powerful multi -layered
prompts as examples. OK, let's start with prompt
one. This one's focused on value investing, right?
Finding cheap cash machines, looking for those
hidden gems. Exactly. We want companies that
are just spewing cash. So we need to target the
top 20 % based on free cash flow yield. And for
anyone listening, free cash flow yield is basically
just how much cash the business generates compared
to its stock price. It's a raw measure of cash
efficiency. But just being a cash machine isn't
enough. There's a crucial constraint. The market
has to be undervaluing them still. Yes. That's
key. So we add the condition. Their price to
earnings ratio, the PE, has to be 15 % lower
than their industry peers. The AI needs to check
both things at once. Companies like Qualcomm
showed up in those results sometimes. It works
because you're finding that sweet spot. High
cash generation and a relatively low valuation.
OK, cool. Next, prompt three. Looking for signals
from insiders. This is about watching what the
execs are doing with our own money. Right. The
prompt tells the AI to scan recent insider training
data, say, the last 90 days. But we're only interested
in large open market purchases. That means the
executive actively decided to go out and buy
stock on the market. not just getting options
or grants. And the amount matters? Yeah, we set
a threshold, like over $100 ,000, and focused
on key roles, CEO, CFO, the top people. We want
significant buys by people who know the business
best. We tell the AI to ignore planned sales
or stock grants. OK, and there's another critical
filter here. You only want to see this buying
if the stock price has actually gone down over
the last six months. Why is that combination
so potent? Because think about it. The boss,
the person who arguably knows the company's real
future prospects better than anyone, is putting
down serious cash to buy more stock precisely
when the market sentiment is negative and the
price has fallen. It's often a strong signal
that they believe the current low price is a
mistake, that there's value the market isn't
seeing. Interesting. Okay, finally, prompt 10.
Finding boring but safe income. This one's tailored
for dividend investors looking for reliability.
Yeah, this is all about safety and consistency.
We focus to search on typically defensive sectors,
utilities, consumer staples, industrials. Then
we layer the conditions. They need a solid dividend
yield, say, above 3 .5 percent. Okay. And crucially,
a safe payout ratio. below 70%. And the payout
ratio, just quickly, that's the percentage of
the company's profits that it uses to pay the
dividends. So lower means more cushion, more
safety. Exactly. A lower payout ratio means they
can likely sustain and even grow the dividend,
even if earnings dip a bit. And then the final
safety net. We demand a history. they must have
increased their dividend for at least five straight
years. Companies like Duke Energy or Coca -Cola
often pop up here. It's a very disciplined filter
designed to find steady companies that reliably
pay and grow their dividends without taking excessive
risks. You know, that kind of systematic approach,
looking for specific data points, it also helps
find these weird market moments, these anomalies,
like that Coinbase trade example you mentioned.
Oh yeah, that was a classic case of the hidden
asset strategy playing out. For a brief window
in, I think it was early 2023, Coinbase's total
market value, its stock price times shares, was
actually lower than the amount of cash and equivalents
they reported holding on their balance sheet.
Wait, how does that even happen? How does the
market make such a seemingly obvious mistake?
Pure fear, basically. It was the depths of the
crypto winter, right? Investors were panicking
about everything crypto related, and that fear
just overwhelmed the basic balance sheet math
for a while. But systematic research, the kind
that specifically flagged situations where cash
holdings exceed market cap, would have instantly
highlighted that. It screamed limited downside
risk with potentially huge upside if sentiment
just returned to normal. So if we kind of zoom
out and tie this all together, this AI -driven
approach, it's systematic, it's active, it's
definitely not passive index investing, and it's
not random guesswork either. The real power is
in how you structure your research process. Right.
It starts with having a clear, testable idea.
your own conviction. Maybe it's NEFL, maybe it's
something else. Like, I believe companies investing
heavily in R &D during downturns will win. Whatever
it is, make it specific. Then use the AI tools,
like stacking Lego blocks of data, you know?
Let the AI do the heavy lifting of gathering
the info quickly based on your rules. Don't get
bogged down in manual data entry. And crucially,
go beyond just yes -no. Use those scoring systems,
like 1 to 10, to capture the nuance. That detail
is often where the real edge lies. And finally,
test your ideas. Use historical data to see if
your hypothesis actually held water in the past.
It doesn't guarantee future results, but it builds
confidence. And it's important to remember, the
goal here isn't some magic formula, some get
-rich -quick secret that works forever. It can't
be. The real goal is making slightly better,
more informed decisions consistently over a long
period of time. That's how genuine wealth is
typically built. Absolutely. And of course, we
have to say this deep dive is purely for educational
purposes. It is definitely not financial advice.
And past performance, as we saw even with NEFL,
never guarantees future results. Yeah. And it's
worth stressing, if you do use a quality filter
like NEFL, that strategy inherently requires
patience. You likely need a long time horizon,
think 10 years or more, for those kinds of quality
factors to really play out. And always, always
manage your risk and make sure your overall portfolio
is diversified. Don't bet the farm on any single
strategy. For sure. But now, you've got the tools.
You've seen the framework. You have some powerful
prompt examples. We really hope you feel encouraged
to open up your favorite AI tool right now and
just test one of these strategies. Start building
your own systematic approach today. Thank you
for taking this deep dive with us.