PromptSharp › Daily briefs › Finance › August 4, 2026

Finance prompt of the day

August 4, 2026 · for Investment banking, sales & trading, equity research, FP&A. One sharp, copy-paste prompt — free, every weekday.

You are a PM building a scenario framework for [TICKER]. I will give the thesis and key drivers. Produce:
1. THREE scenarios (bull, base, bear) each with: the 2-3 driver assumptions, a rough valuation/return implication, and a subjective probability (state it's subjective).
2. A TABLE mapping each driver to the scenario it most affects.
3. KILL CRITERIA: the specific, observable events that would invalidate the thesis and trigger a reassessment.
4. The single variable the outcome is most sensitive to.
Use only inputs I provide; label every probability and return as an estimate to be verified, never a forecast of fact.

Rules: Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it. Mark every claim I should verify externally before relying on it. Never use, infer, or request material non-public information (MNPI) or client-confidential data.

What changed for Finance

How to run “Bull / bear / base scenario tree with kill criteria”, step by step

The situation this prompt is built for: You want a structured scenario framework for a name with explicit probabilities and what would kill the thesis. Below is exactly what to feed it and what comes back — no model-specific tricks, it runs the same in any chat AI.

What each placeholder does

Demo profile for the example fills: a equity research associate on a mid-cap sector coverage team, working in Excel, a chat AI model, and your firm's data terminal. Swap in your own context — or save it once at /profile and copied prompts arrive pre-filled.

• [TICKER] — this is the input the whole output quality hangs on. Demo fill: your own ticker — one or two concrete lines beats a paragraph of vague context. Leave it vague and the model pads with boilerplate; make it concrete and every section downstream sharpens.

What the model hands back

The prompt forces a fixed deliverable shape, so you get a document, not a ramble:

1. THREE scenarios (bull, base, bear) each with: the 2-3 driver assumptions, a rough valuation/return implication, and a subjective probability (state it's subjective).
2. A TABLE mapping each driver to the scenario it most affects.
3. KILL CRITERIA: the specific, observable events that would invalidate the thesis and trigger a reassessment.
4. The single variable the outcome is most sensitive to.

Why this structure works

Scenario discipline plus explicit kill criteria is what separates a thesis from hope; naming the observable events that would prove you wrong builds the sell discipline in before you're anchored.

On Pro, the scenario drivers and return framing match your strategy (deep value vs GARP vs quality-compounder) and holding period from your saved profile.

When to use it — and when not to

Reach for it when

• You want a structured scenario framework for a name with explicit probabilities and what would kill the thesis.
• You can actually supply the inputs it asks for ([TICKER]) — this prompt is an amplifier for real context, not a substitute for it.
• You need the output in a shape you can forward as-is — the fixed structure above is the point.

Skip it when

• You don’t yet have the source material — the prompt is built to refuse to fake it. Its own guardrail: “Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it.” With nothing to work from, you’ll get a list of “not provided” flags, which is honest but not useful. Collect the inputs first.
• The task is genuinely one sentence long — a structured prompt earns its overhead when the output has parts. For quick one-off questions, just ask.

Adapting today’s prompt for adjacent roles

“Bull / bear / base scenario tree with kill criteria” sits in the Investment Management lane of the finance pool. If your seat is one desk over, these are the same craft-move rebuilt for the neighbouring workflow — pulled from the same curated pool, each free in full at its permalink:

You are an equity/IB analyst constructing a trading-comps universe for [TARGET]. Do the following in order: 1. Propose a peer SET grouped into tiers (pure-play,…

You are a trader mapping catalysts for [TICKER / theme]. I will give the known events (earnings, data, product, regulatory, macro). Produce: 1. A CATALYST calendar…

You are a valuation analyst reconciling a multiple gap. [COMPANY A] trades at [multiple] and [COMPANY B] at [multiple]. I will give their financials. Produce: 1. A…

Common failure modes (and the fixes)

• Failure: letting the model drift past the prompt’s own guardrail — “Do not invent, estimate, or extrapolate any figure — if a number is not in what I give you, write "not provided" and flag it.” Fix: keep that line in when you edit the prompt; it exists because this is exactly where outputs go wrong without it.
• Failure: letting the model drift past the prompt’s own guardrail — “Mark every claim I should verify externally before relying on it.” Fix: keep that line in when you edit the prompt; it exists because this is exactly where outputs go wrong without it.
• Failure: letting the model drift past the prompt’s own guardrail — “Never use, infer, or request material non-public information (MNPI) or client-confidential data.” Fix: keep that line in when you edit the prompt; it exists because this is exactly where outputs go wrong without it.
• Failure: filling [TICKER] with a vague summary. The output can only be as specific as this input — generic context in, generic deliverable out. Fix: paste raw specifics (real names, real numbers, real constraints), then trim the model’s output, not your input.
• Failure: accepting the first pass. Fix: reply with one line — “now cut everything that is generic to any company and keep only what is specific to mine” — the cheapest quality doubling available.

Where AI is landing for investment banking right now

Context for today’s prompt, from the same screened sources the daily brief reads. Our read, with sources linked — the pattern across items like these is consistent: the professionals getting leverage from AI are the ones feeding it real working context, which is exactly the muscle today’s prompt trains.

• AI Financial Advice: Supply, Demand, and Life Cycle Implications (arxiv) — We ask a representative sample to write prompts seeking spending and investing advice from LLMs, then simulate the lifetime effects of following the advice under realistic asset…
• Microsoft Earnings, Microsoft vs. Meta, The Efficiency Payoff (rss) — Microsoft's earnings were compelling because they showed a clarity of strategy, lower costs, and a tangibility of application. The reason why is scarier.

Quick answers

Is “Bull / bear / base scenario tree with kill criteria” free to use?

Yes — every weekday issue of the PromptSharp Finance publishes one full pool prompt free on the web, and it stays free in the archive. Pro is the daily full prompt set, personalization, and MCP delivery — not a paywall on this page.

Which AI model does this prompt work with?

Any of them. Every PromptSharp prompt is model-agnostic plain text — ChatGPT, Claude, Gemini, Copilot, or a local model. No plugins, no custom GPTs; paste and run.

How is the finance prompt of the day chosen?

Deterministic rotation over the curated finance pool — currently 75 prompts across 5 sections — the same single source the paid brief reads. Same date, same prompt: the archive never silently changes under you.

What goes in the [BRACKETED] placeholders?

Your context — the walkthrough above covers each one. The short rule: the more concrete the fill (real names, numbers, constraints), the sharper the output. Save your details once at /profile and web copies arrive pre-filled.

How do I get this in my inbox instead?

The capture form above — PromptSharp Finance status is honest: live briefs send every weekday; pre-launch verticals email their free list the day the email edition starts.

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