You know, investment research, it used to be
all about friction. Oh, yeah. Days, sometimes
weeks, buried in SEC filings. Exactly. Trying
to find that edge meant manually cross -referencing
data, wrestling with just, well, information
overload. Now it feels like we're seeing institution
quality analysis, deep, contextual, auditable,
almost instantly. And Chad GPT -5 seems to be
at the heart of this shift. It really does. This
feels... Well, profound, because it's maybe the
first AI model that feels reliable enough, trustworthy
enough for serious real money financial decisions.
Welcome to the deep dive. Great to be digging
into this. Today, we're going to try and peel
back the layers on how professional investors
are adapting. What are they actually doing with
this? Yeah, the practical application. Our mission
is to explore the structured methods they're
using. We'll look at how AI tackles traditional
fundamental analysis, the stock picking side.
And then the technical side, too. Yeah. Chart
patterns and timing. Exactly. And crucially,
we want to uncover the prompts, the master prompts
they use, and maybe most importantly, the human
checks. The verification steps needed to keep
this powerful tool, well, safe. Absolutely. What's
really striking, I think, is how this seems to
address the old trust problem. The hallucination
issue. Right. For ages, AI was basically unusable
for serious finance because of it. Models would
just invent numbers. Make things up. Invent figures,
create fake trends, make statements with zero
factual basis. It turned analysis into something
dangerous, like fiction. That risk. I mean, the
AI just inventing a crucial number. That's the
ultimate deal breaker in finance, isn't it? Completely.
So what makes CGPT -5 the breakthrough here?
Is it just smarter or is it something else? I
think it's less about raw intelligence and more
about being verifiable, auditable. It really
comes down to audibility. We're seeing a huge
drop in those hallucination rates. But the big
thing, proper source citing. Like footnotes.
Exactly like footnotes, like an academic paper.
It shows you precisely. which SEC filing, which
press release, which 10K generated that specific
data point. Okay. That makes sense. It's building
trust through transparency. Yeah. So here's a
question then. If the AI is getting so much more
accurate and it's citing its sources perfectly,
what's the main competitive edge for it compared
to, say, a really skilled human analyst doing
the same deep dive manually? Yeah. That's the
core question. What's the edge? I'd say the edge
is the scale of contextual awareness analyzed
instantly. Speed and breadth beat manual review
every time when you're talking hundreds of documents.
Right. Scale. And that scale brings us neatly
into fundamental analysis. The classic approach.
Trying to figure out a company's real intrinsic
value, looking at its financials, its market
position, the management team. All that good
stuff. And the AI here, it's not replacing the
analyst, is it? It's more like a super -powered
research engine. Precisely. The key is effective
delegation. You, the investor, you set the strategy,
the framework. The AI does the heavy lifting,
the relentless data sifting. So you use something
called a master prompt framework. That's right.
It starts broad. You define your investment strategy
first. Are you looking for growth, value, focused
on specific sector like tech or healthcare? You
set the direction. Exactly. But then you get
specific. Once the strategy is set, what's like...
The most critical hard number you ask the AI
to screen for first. Is it always about cash
flow? Positive free cash flow growth is usually
a pretty strong sign, yeah. Shows the business
is actually generating cash. It's sustainable.
Makes sense. But you need safety nets too, right?
So the quantitative screening part needs hard
metrics. Things like a liquidity ratio maybe
above 1 .0, high gross margins, say, north of
40%. Right. You're building criteria. Yeah. It's
like stacking Lego blocks of data, building a
solid foundation. And then there's a third step,
which handles the, let's say, squishier stuff.
Yeah. The qualitative screening. Management track
records. Market sentiment. Yep. Leadership quality,
competitive modes. Does the company have a real
advantage? What's the buzz? The analyst consensus.
Is management stable? Let's make this concrete.
The source material used NVIDIA as an example.
How does the AI output look? Well, when structured
correctly, it doesn't just dump numbers on you.
It synthesizes. Okay. So for NVIDIA, it might
highlight, say, the massive revenue growth and
data centers up, what was it, 427 % year over
year? And the high margins. But it goes beyond
just the numbers. Critically, yes. It translates
that data into competitive intelligence. It points
out how NVIDIA's CED ecosystem creates huge switching
costs for customers. That's a qualitative insight
derived from the data. Got it. But the key quality
check is? Pricability. Always. Every key number,
every claim needs that footnote back to the official
source. The specific 10Q or 10K filing. no exceptions
that makes sense you know you mentioned prompts
i still wrestle with prompt drift myself sometimes
getting the ai to consistently apply the exact
same criteria over time oh absolutely it's a
real challenge and beyond just consistency we
also need the ai to consider context right like
how does a recommendation fit with my existing
portfolio or the current economic cycle rising
rates falling rates That changes things. Hugely
important. What does that prompt drift actually
look like? Does the AI just get lazy or does
it subtly change the rules on you? It's kind
of more insidious than laziness. It's like the
AI subtly shifts the emphasis of your criteria
based on past chance. Or it might just... Forget
a key negative screen you set up. Yeah. So if
you ask it for value stocks, three days running,
maybe on day four, it slightly relaxes your required
liquidity ratio unless you explicitly state it
again every single time. Constant vigilance required.
OK, so thinking about fundamental analysis, if
the AI is doing all the screening, analyzing
financials, qualitative factors, what's one big
challenge where human oversight is still absolutely
critical? Defining the right criteria up front
and crucially aligning those recommendations
with the broader economic. cycle context the
AI gives you data you provide the wisdom all
right so the AI has helped us find potential
value through fundamentals the next big question
is when when to potentially buy or sell and maybe
more importantly how to manage the risk involved
timing and risk management and that speed and
data processing power mmm it leads us right into
part two Using AI for technical analysis. This
is where it gets really interesting for traders.
Technical analysis aims to predict price moves
by studying past chart patterns, price and volume
action. Right. And the AI becomes this expert
chartist, spotting complex patterns across hundreds,
maybe thousands of assets way faster than any
human possibly could. And the technical master
prompt for this. It's demanding. It needs two
types of data. It does. That's a key innovation,
I think. You have to give it the visual, the
screenshot of the candlestick chart. That's for
the pattern recognition part. Okay, the picture.
But then you also feed it the raw numbers, the
CSV file with the OHLCV data. Sorry, can you
just quickly define OHLCV again for everyone?
Sure thing. It's open high, low, close prices,
and volume for each period. It's the mathematical
backbone. So the picture and the numbers. Exactly.
The numbers validate what the AI thinks it sees
in the picture. ensures precision ah so that
dual input it creates like an immediate internal
check a verification system built right in precisely
the visual pattern gets confirmed or denied by
the hard mathematical data that must cut down
errors significantly and it gives you exact measurements
for setting stops targets risk management spot
on wow that combination speed and precision it
really changes the game for a trader doesn't
it whoa imagine scaling that analyzing, I don't
know, a billion potential trading setups every
single day. The scale is mind -boggling. And
that precision shows up in the examples. Like
the head and shoulders pattern discussed for
NVIDIA, that's a classic bearish reversal setup.
Okay. The AI doesn't just flag it and say H &S
pattern here. It explains why it sets the criteria,
the specific price structure, the volume action
that confirms it. And then it gives you the actionable
levels. Exactly. Precise levels. Here's your
potential entry point. Here's the stop loss,
the price where the pattern is clearly invalidated.
And here are the profit targets, often based
on a measured move calculation. So it hands you
a structured trade idea, potentially with a favorable
risk reward ratio, maybe like 1 to 2 .2, ready
to go. That's the goal. And you can get more
advanced, too. Things like multi -time frame
analysis. Checking if a pattern on, say, an hourly
chart aligns with the trend on the daily chart.
Exactly that. Adds conviction. And you can integrate
it with option strategies, identifying key strike
prices near the AI's levels, estimating timelines
for choosing expirations. OK, so we've got the
analysis, maybe even a specific trade idea from
the AI. What are the crucial implementation steps?
How do you actually make this research effective
day to day? You absolutely need a structured
daily routine and just relentless quality control
of whatever the AI gives you. It's not magic.
Mid -roll sponsor read placeholder. So that structured
routine. Yeah. It's totally non -negotiable.
You can't just casually chat with it and expect
consistent results. Yeah. You need a daily analysis
routine. Like a checklist. Sort of. Morning prep
could involve checking overnight futures, seeing
what economic data is coming out that day, analyzing
pre -market gaps in your watch list stocks. Okay.
Then, at the end of the day, an evening review,
a performance post -mortem, what worked, what
didn't, and then updating your watch list using
fresh AI screenings based on today's action.
Discipline is key. Absolutely. And quality control,
the QC part, this is where the human judge becomes
roaring back. It's critical. AI is a tool, an
incredibly powerful one, but it's not a replacement
for your own thinking. So always check the sources
it provides. Always check the footnoted sources.
Always try to cross -reference key data points
independently if you can and just use basic sanity
checks. Meaning, if the AI suggests a tiny biotech
stock should suddenly jump 500 % based on some
obscure regulatory filing it found. You probably
want to verify that filing wasn't just, I don't
know, a draft or misinterpreted. Does it make
sense in the real world? That systematic skepticism,
it saves you from potentially huge errors. And
one really powerful technique for complex decisions
is using chain of thought analysis prompts. Asking
the AI to show its work. Basically, yeah. You
ask it to walk through its reasoning step by
step. First, check the fundamentals. Then confirm
with technicals. Then assess the risk. Then,
and only then. give the final verdict or recommendation.
So you can follow its logic. Exactly. It makes
the reasoning auditable, not just a black box
answer. But that complexity, all that data, doesn't
it risk leading to, you know, analysis paralysis?
Too much information can't pull the trigger.
That's a very real risk. Information overload
is easy with these tools. So how do you fight
that? We use strict filters and the prompts.
You explicitly tell the AI, summarize your findings
in exactly three bullet points. Two bullets only.
Yep. Maybe. The single strongest bullish factor,
the biggest risk or concern, and a clear, concise
action recommendation. Constraints force clarity.
Cut through the noise. That's smart. And what
about the risk of just believing the AI too much?
It sounds so confident sometimes. Overconfident.
Huge pitfall. To counter that, you use skepticism
prompts. Asking it to argue against itself. Pretty
much. You force the AI to play devil's advocate.
Ask it. What assumptions did you make here that
might be wrong? Or why might sophisticated investors
be selling this stock right now, even if it looks
good on paper? Challenging his own conclusions.
Building that critical challenge into the process,
not just as an afterthought. That's how you aim
for a sustainable edge, I think. Okay, so let's
try and wrap this up, synthesize the big idea
here. CGPT -5 seems like a genuine leap forward.
It's making institutional -grade research much
more accessible. Definitely feels that way. But
it's not about just handing the keys over to
the machine. It's more like a... A structured
collaboration. Yeah, a partnership. Between human
intuition, which sets the strategy, asks the
right questions, and this advanced AI, which
handles the massive scale of data processing
and synthesis. That's a great way to put it.
And the crucial takeaway, I think, no matter
how smart this tech gets, is that you, the investor,
you must still verify the critical data points.
You have to understand your own personal risk
tolerance. You have to manage your position sizing
appropriately. The AI provides guidance, but
the responsibility is still yours. Absolutely.
The AI is your assistant, a powerful one, but
it's never your master. You own the risk. So
here's maybe a final thought to leave you with.
If the AI can flawlessly analyze financials in
seconds, if it can spot complex chart patterns
instantly, calculate precise risk -reward ratios,
all using auditable sources, what percentage
of your investment decision process should still
remain purely intuitive? That's a deep question.
It's something worth exploring, I think, as you
design your own systematic risk -managed approach
in this new era. Where does human gut feel fit
in now? We definitely encourage you to start
thinking about structuring your own daily blueprint,
maybe focusing first on those critical quality
control and verification steps we talked about.
Start small. Build the process. Exactly. We'll
see you next time for the next Deep Dive.