When you sit down with an artificial intelligence
tool to research your investments, are you simply
using a lightning -fast search engine, BEAT,
or is that AI actually functioning as your personal,
you know, dedicated financial analyst? That is
the crucial distinction, yeah. And welcome to
the Deep Dive. The difference, as our sources
are showing, really comes down to the quality
of your command, of your prompt. Most investors,
they still rely on simple, kind of one -line
questions. But stay, we're giving you a shortcut,
really, to maximizing rigor. Right. We're looking
at sources that treat the prompt, the instruction
you give the AI like a complete blueprint, almost
like a multi -part structure designed to generate
an organized, deep research report, not just,
you know, a list of facts. Exactly. Our mission
today is to give you that blueprint. seven detailed
examples. We're showing you how to move from
basic data check stuff like that to complex stock
screening and even qualitative analysis. You're
going to learn how to transform an AI, something
like perplexity, into a disciplined systematic
research partner. Let's dive right into the core
analysis. OK, let's unpack this first scenario,
then. We really need to move far past just asking
if a stock went up or down. To gain a true edge,
you've got to understand the why behind that
move and, crucially, what the wider market thinks
now. Right. So our case study focuses on NVIDIA
after a massive price jump. A structured analysis
here kicks off with a four -part prompt. And
the first output it gives you is a performance
comparison. And that performance gap is, well,
it's staggering, isn't it? Our sources note that
in one recent week, NVDA stock delivered an astonishing
game, 21 .5%. 21 .5%. Now, hold that number against
the sector benchmark. The VanEck semiconductor
ETF, that's SMH, tracks the whole industry. it
only managed an 8 .2 % climb in the same period.
So the massive outperformance, it confirms the
catalyst was company specific. It wasn't just
a rising tide lifting all boats, you know. Right,
it singles out Nvidia. Which brings us directly
to the primary catalyst. What was the specific
needle moving news here? It was the GTC 2025
conference and specifically the introduction
of the new Blackwell Ultra GPU for data centers.
And get this, this wasn't just some small incremental
update. Public tests highlight that Blackwell
Ultra was 30 % faster for key AI training tasks
than the older model. 30%. That's a huge leap.
Yeah. That kind of performance jump immediately
shifts the calculus for analysts. You can see
it happen. And the analyst reaction was swift
and quantifiable, too. HSBC upgraded NVDA from
hold. to buy. Goldman Sachs significantly raised
its price target, moving it from $150 up to $180.
I mean, the market perception just changed overnight.
And the change in valuation is maybe the most
important output here. Before this rally, the
stock's forward PE ratio was sitting around 35
times earnings. OK, 35x. And after that 21 .5
% jump, that forward PE shot up to 42 .5 times.
That is a substantial increase. It really is.
The market isn't just saying NVDA is worth X
amount today. By accepting that much higher ratio,
42 .5, they are explicitly pricing in huge, almost
guaranteed future growth, which means they're
willing to pay a massive risk premium for those
projected earnings down the line. OK, let's just
unpack that premium for a second. How does that
PE jump going from 35x to 42 .5x, how does that
actually quantify investor belief in NVDA's future
growth? Well, the increase confirms investors
expect significantly higher future earnings.
It moves beyond just optimism into actual financial
commitment. Right. That shift from just analysis
to actual commitment. That's key. Absolutely.
Now, moving on. This is where you stop being
like a manual data clerk and really start acting
as a portfolio manager. The next two prompts
focus entirely on automation and scaling your
analysis using structured reports. OK, so prompt
two is designed for a daily portfolio briefing.
You basically stop manually checking individual
events every single morning and you let the AI
compile the heavy lifting for you. Exactly. This
structured report delivers four key components.
First up. technical analysis. So it's tracking
price change, volume compared to the 20 -day
average, and those essential support or resistance
levels. Then you get fundamental news. And this
includes any urgent SEC filings, like a Form
8K. For those who may be unfamiliar, that signals
a major corporate change or some crucial event
that needs immediate attention. It's important
stuff. Definitely. The report also captures analyst
updates and critical macroeconomic context. Our
sources showed an example where the August CPI
data, the inflation number, came in at 0 .4%.
That was higher than expected. Now, that kind
of macro news needs to be filtered down to specific
stock impact. You need to see how it connects.
And the report does that. It delivers those targeted
results. For instance, TSLA faced selling pressure
dropping 2 .1%, breaking below its key $250 support
level, potentially due to production warmers,
maybe linked to the economic outlook. Meanwhile,
Microsoft, MSFT, it rose 1 .5 % on trading volume
that was 30 % higher than its average. The AI
connects the dots between the CPI number and
the specific stock reactions almost instantly.
And that speed and organization leads directly
into prompt three, the complex technical screening.
This is where you transform an entire multi -layered
trading strategy into just one single command
for the AI. OK, so you define the specific bullish
criteria for the AI. You're basically asking
it to search the entire S &P 500 and find only
the that meet all three specific conditions at
the same time. Pretty rigorous. First, it's got
to be exiting oversold territory. That means
the 14 May Relative Strength Index, or RSI that
measures price momentum, has recently moved above
the 30 level. Second, you need a bullish MEC
-ED crossover. That indicates a potential shift
in longer -term momentum is underway. OK. And
the third hurdle is volume confirmation. You
need that daily trading volume to be at least
50 % higher than the 20 -day average. That proves
the move has real conviction behind it. Yeah,
just listing those three complex criteria simultaneously
makes my head spin thinking about checking even
20 stocks manually for all that. Honestly, yeah.
Trying to check the RSI and the MechEd crossover
and the volume confirmation across a full watch
list, that's usually where I just kind of give
up and maybe resort to a hunch. This prompt solves
that exact pain point. It's a vulnerability I
definitely admit to having myself sometimes.
Right. We've all been there. And the resulting
report from the AI, it found three opportunities
that met all those criteria. Adobe, Qualcomm,
and Caterpillar. And the data confirmation was
right there. QCOM, for example, it showed a plus
72 % volume change validating that signal. OK,
so drilling down on that, what is the main benefit
of screening using three complex criteria simultaneously
like this? It efficiently finds opportunities
that exactly match a specific multi -layer trading
strategy. And importantly, it eliminates human
error or oversight. Efficiency and accuracy.
Makes sense. a mid -roll sponsor read placeholder.
Welcome back. So far, we've covered hard data,
automated reports. But where the real alpha,
the edge, is often found, it's not just in the
numbers. It's often in the qualitative analysis
reading between the lines of management communication.
Exactly. Prompt4 focuses on going inside an earnings
call. This process extracts management sentiment,
identifies recurring themes in the Q &A session,
which often reveal way more about future strategy
than just the basic financials do. Let's use
the Microsoft MSFT earnings call pace study here.
Part one analyzes the key performance indicators,
the KPIs. The AI extracted that Azure cloud revenue
grew by 42%. Pretty strong number. Yeah, but
the fascinating part is the acceleration. Azure
growth was actually slightly higher than the
40 % reported in the previous quarter. Now why
is that tiny 2 % difference such a strong signal?
It suggests operational momentum is actually
building, not slowing down. Interesting nuance.
OK, then the Q &A theme section. This is where
the structure prompt really shines, I think.
The AI identified the top three concerns that
analysts kept hammering away at during the call.
Number one, how Microsoft plans on actually monetizing
its co -pilot AI services. Number two, maintaining
its cloud advantage over rivals like AWS and
Google. And number three, the looming threat
of increasing global regulation. Those were the
hot topics. And then there's the sentiment analysis.
This is something that's incredibly hard for
a human to track consistently across hours of
audio. The AI detected a consistently confident
and assertive tone from the management team throughout
the entire call. OK, and here's where it gets
really interesting, I thought. When addressing
the competitive landscape, the CEO made a highly
specific strategic point. He emphasized offering
a complete system from the computer chips all
the way to the final apps. That's a direct quote
pulled by the AI. And this was framed as their
unique advantage over companies that only offer
single sort of. point solutions. Yeah, that quote,
that single quote, is the kind of detail that
informs your investment thesis way better than
just looking at a balance sheet sometimes. It's
really a statement about market domination strategy,
not just market share numbers. So digging into
that sentiment piece again, what did the sentiment
analysis reveal about Microsoft's competition
strategy specifically? Management confidently
focused on promoting their whole end -to -end
system, their ecosystem, rather than just competing
on specs for single products. Okay, moving into
our final segment now. We're looking at using
AI as more of a strategic radar system. This
involves tracking the entire competitive landscape
and then filtering the market based on advanced,
sometimes complex investment philosophies. Right,
so Prompt 5 is your weekly industry intelligence
report. You're effectively using AI as a sophisticated
radar system. It tracks the entire sector, not
just the stocks you happen to own or follow closely.
Exactly. The first part tracks the competitive
landscape. For example, the sources noted a key
strategic partnership between AMD and Oracle
Cloud to deploy AMD's new MI -400X accelerators.
Now, that is a direct, measurable challenge to
Nvidia's dominance in the data center space.
You need to know that. Part two handles emerging
trends. This is where the AI spotted the sovereign
AI trend. That's countries like France and India
starting to build their own national large language
models. This creates an entirely new global market
for infrastructure. and ship companies. That's
a big one. And part three tracks venture capital
flow. Where's the smart money going? We see an
example like a startup called Cerebral Dynamics
focusing on AI drug discovery, raising a staggering
$120 million. This immediately flags where sophisticated
investors are deploying capital right now. That
sovereign AI trend. Wow. I mean, I would have
completely missed that just scrolling through
normal headlines. Whoa. Imagine automatically
tracking every single global trend like that
without reading hundreds of news sources every
single week. That's like having a bespoke spy
satellite just for market movers. Incredible.
Right. That kind of comprehensive radar shifts
the conversation from just reacting to data passively
to actively forming strategy. So we've covered
five types of reports now. Let's jump to our
final technique. This is prompt seven, which
transforms an entire investment philosophy into
basically machine code. OK, prompt seven is the
JARP screening, growth at a reasonable price.
Now, this is a highly nuanced philosophy, right?
It blends two separate concepts, quality and
value, into one precise interlocking set of machine
searchable criteria. Man, the criteria are demanding.
They're layered. Yeah. You require scale, first
off market cap over $100 billion, then consistent
growth over 15 % revenue growth for three consecutive
years, plus high quality needing over 60 % gross
margin and over 20 % return on equity. Pretty
tough gates. And critically, the requirement
for a reasonable price. Specifically, the forward
PE ratio must be below its own five -year historical
average. That's complex screening. It forces
the AI to consider relative historical valuation,
not just the current price level. OK, but wait
a sec. Isn't defining reasonable price simply
by its historical average maybe a bit too simplistic
for a machine? Doesn't the broader economic context
like interest rates matter there? That's a fair
point. It does matter in the real world. Absolutely.
But the beauty of these structured rules is that
the prompt isn't trying to capture all possible
context. That would be impossible. It's trying
to capture one specific actionable trading inefficiency.
Finding stocks that are high quality and still
growing but seem to be temporarily mispriced
compared to their own established historical
norm. It's a very focused task. I see. So it's
finding a specific anomaly based on its own history.
And the results from the source material showed
that Microsoft, MSFT, and Adobe ADBE met all
these strict requirements at that time. Notably,
Microsoft's PE was 5 % below its five -year average,
and Adobe's was actually 10 % below. So it efficiently
finds these high -quality opportunities only
when they're temporarily potentially undervalued
relative to themselves. So thinking about that
JARP prompt, what would you say is its main accomplishment?
What does it really achieve? It efficiently finds
high -quality stocks only when they appear temporarily
mispriced relative to their own established historical
valuation range. That really is the core lesson
we wanted to share today, isn't it? The quality
of the AI output you get is directly proportional
to the quality, the structure, the detail of
your input. These detailed multi -part prompts,
they really act as a necessary blueprint. Yeah,
they save you countless hours of routine research.
Absolutely, yes. But far more importantly, I
think, they significantly improve the rigor and
the quality of your analysis by forcing the AI
to synthesize multiple complex data points simultaneously.
simultaneously, something that's really hard
for us humans to do consistently. Now, just a
quick but very necessary word of caution here.
The information we discussed today, including
the specific stocks and metrics like NVDA or
MSFT, it's purely for educational purposes only.
This is not investment advice. Please always
perform your own due diligence and speak to a
qualified professional before making any financial
decisions. Indeed. Very important disclaimer.
We focus heavily today on using these structured
prompts for analysis, you know, reacting to and
dissecting the data as it arrives. But what if
we sort of reverse the process? How can these
automated systems become fully proactive? Could
they be constantly generating new customized
investment philosophies, maybe new complex criteria
for us to simply test and explore, beat? That
could fundamentally change how we even discover
opportunities in the first place. That thought
alone should really inspire you listening. Start
by building your first detailed prompt blueprint
today. Use these examples and just see how much
faster and frankly smarter your research becomes
even this week. Until the next deep dive.