4 Steps to Tell if a Stock Panic Is an Opportunity or a Trap

In under 1 hour with AI...

Hello, Fellow Stock Pickers

The past few weeks were wild.

Market darlings got crushed:

• Novo Nordisk lost 57$ billion in a day after a profit warning and CEO change

• ASML wiped out 130 billion on fears of a 2026 slowdown.

• LVMH is down 30% this year

When the market reacts this violently to headlines, history says one thing:

“Opportunity comes in moments of maximum fear.”

This is not about chasing crashes blindly…

Our job is simple: cut through noise and spot real opportunities.

Good news. AI makes this easier than ever.

Here’s the 4-step process I use to go from “Why is this stock falling?” to making an informed decision fast.

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Today’s case: Edenred & Pluxee:

Let’s put it to the test with Edenred & Pluxee.

From time to time, entire sectors fall out of favor even when the fundamentals are rock-solid.
The employee benefits space is one of them. Edenred and Pluxee have been hammered despite:

• EBIT margins above 30%

• Float that fuels 10% annual growth

• Free cash flow margins above 25%

• Now trading at just 6× EV/EBIT

The sector’s been out of favor for two years.
Let’s run it through my 4-step AI process to see if this is an opportunity… or just more noise.

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Step 1:Analyze the Business Model

Goal: Know exactly how the company makes money and why customers keep paying.

If you don’t get this, every headline looks scary.

What I look for:

• What’s the product? What problem does it solve?

• Who pays? How essential is it? Switching costs?

• How’s the money made? Pricing, streams, recurring vs one-off.

• What’s the moat? Network, contracts, regulation, brand.

• What drives growth? Price, volume, expansion, mix.

How I do it in minutes with AI:
Instead of reading 3 annual reports and 2 decks, I run this in DeepSearch/Gemini.

You are a senior equity analyst with deep expertise in the company’s[COMPANY NAME]  industry, 

From all available authoritative and expert sources — including the company’s annual reports (multiple years), investor presentations, earnings call transcripts, reputable equity research, and credible industry reports — provide a deep, structured breakdown of the business model of [COMPANY NAME].

Cover in detail:
1) Products/services and the problems they solve for customers  
2) Customer base: who pays, decision makers, switching costs, must-have factor  
3) Revenue model: pricing approach, streams, recurring vs one-time revenue  
4) Supply side: key suppliers, dependencies, and bargaining power  
5) Competitive advantages/moats (network effects, brand, contracts, regulation, cost)  
6) Industry context: market structure, main competitors, and positioning  
7) Growth drivers: pricing power, upsell, new markets, product expansion, M&A  
8) Risks tied to the model: business, operational, regulatory  
9) Historical evolution: how the model has changed over the last 5–10 years  
10) A one-sentence “money engine” summary — how the company really makes money  

Output requirements:
- 700–900 words  
- Use clear section headers for each part   
- Cite sources with [source name, year, page/section] where possible  
- Flag assumptions as “inferred” if not directly stated  
- End with a **5-point Investor Takeaway**: core strength, key dependency, top growth driver, main risk, biggest unknown

When I ran this on Edenred and Pluxee, the results were mind-blowing.
a report that broke down the business model better than hours of manual research.

Years ago, I spent hours digging into this complex business model.
Now, in just one prompt, AI produced a report that explained it better in a few pages.

And it gets even better:
With one click in Gemini, you can generate an interactive one-pager that explains the business model so clearly:

Step 2:Map the Panic Timeline

Once you understand how a company makes money, the next question is:

When has the market freaked out and why?

Without the timeline, you can’t tell if the market’s reaction was justified… or just panic.

What I look for:

• Every major selloff (and the exact % drop)

• The trigger: earnings miss, regulation, macro shock, peer results, leadership change, etc.

• Patterns: which events recover fast vs. cause lasting damage

Why AI crushes this step:
Normally, mapping this out means digging through filings, news articles, and price charts.
AI can put it together fast and give you a clear picture.

Steal my Panic Timeline Prompt:

Optimized Master Prompt – Panic Timeline Mapping

    Role & Context
    You are a senior equity analyst with expertise in mapping historical market reactions.
    Your goal is to build a source-backed, investor-grade “Panic Timeline” for [Company Name] covering [Start Date] → Present.
    This will be used in a long-term investment analysis to distinguish genuine, thesis-breaking events from short-term overreactions.

    Scope of Search
    – Search all credible, high-quality sources:
    • Official company filings (earnings releases, annual/quarterly reports, investor presentations)
    • Reputable financial media (Bloomberg, Reuters, FT, WSJ)
    • Analyst research notes and sector reports
    • Market data sources (price history, volume spikes)
    – Look beyond headlines: capture both company-specific and macro/sector events.

    Step 1 – Drawdown Identification
    – Identify every significant price drop (≥ [X]% in a day or over a short period) since [Start Date].
    – For each:
    • Date (or date range)
    • % drop (precise)
    • Source link(s) confirming price move and cause

    Step 2 – Event Narrative
    – For each drawdown, provide a 3–5 sentence narrative explaining why it happened.
    – Specify if the driver was:
    • Company-specific (earnings miss, guidance cut, leadership change, fraud, etc.)
    • Sector-related (peer results, regulation, pricing pressure)
    • Macro / market-wide (interest rates, geopolitical shock, recession fears)
    – If multiple causes: rank them by impact.

    Step 3 – Investor Pattern Analysis
    – Summarize key patterns:
    • Which event types cause the biggest drops?
    • Frequency of macro vs. company-specific drivers
    • Any recurring risks or overreactions
    – Identify whether past drawdowns recovered quickly or led to lasting damage.

    Output Requirements
    – Section 1: Table → Date | % Drop | Cause (short) | Source(s)
    – Section 2: Bullet list of narratives (one per event)
    – Section 3: Pattern summary with insights for long-term investors
    – Only include verified, attributed information — no speculation.
    – Avoid generic “market volatility” unless tied to a concrete event.

    Final Objective
    Deliver a clear, fact-based, and actionable panic timeline that allows an investor to instantly see:

        The when of every major selloff

        The why behind each drop

        Patterns that reveal market psychology vs. true business deterioration

As always, the result is a full document you can read or explore interactively, like this

Step 3 :Assess the Risks

Now you know how the company makes money…
And when the market has panicked before.

Now it’s time for the most important question:

What could actually kill the business ?

This isn’t about vague “market volatility” or “macro uncertainty.”

I want a precise list of the real risks, the ones the company admits, the ones analysts keep talking about, and the ones that have already made the stock drop hard.

What I look for:

• Regulatory & compliance risks (laws, caps, antitrust)

• Competitive risks (new entrants, pricing wars)

• Operational risks (supply chain, key people, tech failures)

• Financial risks (debt, currency, liquidity)

• Strategic risks (bad M&A, failed expansion)

Why AI makes this 10× faster:
Normally, this means combing through years of annual reports, risk factor sections, analyst notes, and market data.
AI can scrape it all, group it, and give you a risk matrix you can actually invest from.

Steal my Risk Assessment Prompt:

You are a senior equity analyst specializing in the [SECTOR NAME].  
Search all credible, expert-level sources — including [COMPANY 1] and [COMPANY 2] official filings (annual reports, risk factor sections, earnings presentations, investor days), reputable financial media, and analyst notes — from the past 3 years.

Goal: Build an exhaustive, fact-based risk assessment.

Deliver in 3 parts:

1. Risk Inventory  
   - Group risks by category: Regulatory & compliance, Competitive, Operational, Financial, Strategic.  
   - For each: specify which company disclosed it, include the exact source excerpt + date.

2. Market Impact History  
   - List risks that triggered significant share price drops since [DATE], with % drop and narrative.

3. Risk Matrix  
   - Columns: Risk name | Probability (Low / Medium / High) | Estimated financial impact (% revenue or EBIT margin) | Overall risk score (probability × impact).  
   - Use evidence-based ranges from filings, analysts, or sector precedent.

The results?
Every time, they’ve been nothing short of mind-blowing.

Gemini give a full report or as an interactive one pager:

Full sector kill list (Top 10 risks):

Country-by-Country Breakdown:

Strict regulation + fintech disruption = a race to the bottom on pricing.

Step 4: See What’s Already Priced In

You’ve mapped the business model.
You’ve tracked every market panic.
You’ve listed the risks that could break your thesis.

Now comes the million-dollar question:

How much of this is already baked into the price?

If regulation risk could cut revenue and margins, I want numbers , worst case, base case, and optimistic.
Because if the market is already pricing in disaster and that’s unlikely to happen, that’s an opportunity.

At this step, AI is your partner, not the decision maker.

It can help you brainstorm scenarios, test assumptions, and simulate impacts.
But the final judgment? That’s still on you.

How to Test It:

1. Drop the Master Prompt Step 4 (below) into a reasoning model like ChatGPT O3 or the ChatGPT-5 Thinking model.

2. Swap in your company’s name.

3. Use multiple exchanges to:

   • Test different scenarios (worst, base, optimistic)

   • Brainstorm ways to quantify each major risk’s impact

   • Spot assumptions the market may be overestimating or underestimating

4. Use the output to decide if the gap between price and reality is worth betting on.

Here’s an example of a prompt I used

    You are a senior equity analyst specializing in the employee benefits sector.
    Search all credible, expert-level sources — including Edenred’s and Pluxee’s official filings (annual reports, risk factor sections, MD&A, earnings presentations) from the past 5 years, reputable financial media, and analyst notes.

    Goal: Quantify the market impact of the top 1–3 risks identified earlier with special focus on regulatory changes affecting voucher/tax rules, fintech licensing, and digital payment compliance — on revenue and EBIT margins, in both worst-case and median scenarios.

    Deliver in 3 parts:

        Evidence Gathering
        – Find every historical instance (past 5 years) where Edenred, Pluxee, or peers in the employee benefits space were affected by regulatory changes.
        – Extract direct quotes, numbers, and context from filings, earnings calls, and analyst reports.
        – If no direct precedent, use comparable events from similar fintech/payment service providers.

        Impact Quantification
        – For each scenario (worst-case, median), estimate the % impact on Edenred’s and Pluxee’s revenue and EBIT margin.
        – Base estimates on historical precedent, peer comparisons, and disclosed sensitivity analyses.
        – Clearly distinguish between company-disclosed numbers and independent/analyst estimates.

        Valuation Context
        – Compare current valuation multiples (EV/EBIT, P/E, FCF yield) to:
        a) Their own 5-year historical ranges
        b) The averages for global payment/benefits peers
        – State whether the estimated regulatory risk appears fully priced in, partially priced in, or not priced in at all.

    Output format:
    – Bullet points for all evidence and data points.
    – A 2×2 table showing scenario impacts (Probability × Impact).
    – A 2–3 sentence conclusion on whether the current valuation reflects the regulatory risk.

    Rules:
    – Only use verified, source-backed data.
    – Clearly cite all sources for every figure.

Don’t forget, at this step, you make the final call.
Always check the bear case to double-check your thesis.
I dedicated my last newsletter just to this topic.

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My Take on edered and pluxee

Even if every regulation in Brazil, Italy, and France hit at the same time, EBIT growth would slow to around 4%.

That’s the worst case and I don’t think it happens. Regulations rarely land all at once.

At today’s valuation, the punishment looks exaggerated.
That’s why I see an asymmetric setup.

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Final Take

Let’s recap.

When panic hits, stocks can drop fast.
The question is are you grabbing a deal, or catching a falling knife?

Here’s my 4-step, AI-powered checklist:

1. Know the business model : Drop the Business Model Prompt into DeepSearch or Gemini. In minutes, you’ll get a full breakdown from filings, decks, and analyst notes.

2. Map past panics : Use the Panic Timeline Prompt in DeepSearch to pull every big selloff, the % drop, and the cause.

3. List the real risks : Run the Risk Assessment Prompt in Gemini to scrape filings, media, and analyst reports. Group them by probability × impact.

4. Check price vs reality : Use the What’s Priced In Prompt in ChatGPT thinking model to test worst cases. Don’t forget: at this step, you are the decision-maker.

Of course, you still have to do the work ,read the reports, think about position sizing, and know your own risk tolerance.

My job here is simple :give you more AI-powered tools to be a better investor and to move faster when it matters most.

Thanks for reading.
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