PromptSharp › Daily briefs › Finance › September 17, 2026

Finance prompt of the day

September 17, 2026 · for Investment banking, sales & trading, equity research, FP&A. One sharp, copy-paste prompt — free, every weekday.

You are an M&A associate building a precedent-transactions analysis for a target I describe: [TARGET, sector, size, geography]. Produce:
1. A TABLE of candidate precedent deals (columns: acquirer, target, announced date, deal value, EV/Revenue, EV/EBITDA, % premium, strategic vs financial buyer, rationale). Include only deals you can name a real basis for; where a figure is not provided to you, write "verify".
2. A short note on which deals are the TIGHTEST comparables and why (size, business model, cycle timing).
3. A CLEAN-vs-ADJUSTED flag per multiple: which are distorted by synergies, control premia, or one-time items and should be normalized.
4. The 2-3 questions to resolve before quoting any multiple to a client.

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 “Precedent-transactions screen + adjusted-multiples framing”, step by step

The situation this prompt is built for: You need a defensible set of precedent M&A deals for a target and a clean read on what buyers actually paid. 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: an 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.

• [TARGET, sector, size, geography] — this is the input the whole output quality hangs on. Each part narrows the answer: target; sector; size; geography. Demo fill: the specific name plus one line of context — e.g. “Meridian Foods, packaged-food carve-out, sponsor-backed, first institutional process”. 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. A TABLE of candidate precedent deals (columns: acquirer, target, announced date, deal value, EV/Revenue, EV/EBITDA, % premium, strategic vs financial buyer, rationale). Include only deals you can name a real basis for; where a figure is not provided to you, write "verify".
2. A short note on which deals are the TIGHTEST comparables and why (size, business model, cycle timing).
3. A CLEAN-vs-ADJUSTED flag per multiple: which are distorted by synergies, control premia, or one-time items and should be normalized.
4. The 2-3 questions to resolve before quoting any multiple to a client.

Why this structure works

Precedent screens live or die on comparability and on separating clean multiples from control/synergy noise — this forces both, plus a verification step so no headline multiple is quoted blind.

On Pro, arrives pre-filtered to your coverage sector's real deal set and your firm's preferred multiple conventions (EV/EBITDA vs EV/Revenue emphasis) from the profile you set once.

When to use it — and when not to

Reach for it when

• You need a defensible set of precedent M&A deals for a target and a clean read on what buyers actually paid.
• You can actually supply the inputs it asks for ([TARGET, sector, size, geography]) — 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

“Precedent-transactions screen + adjusted-multiples framing” sits in the Banking 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 a sales-trader writing a MORNING desk-color note for [asset class / coverage]. I will paste overnight moves, headlines, and levels. Produce: 1. A 2-3 sentence…

You are a buy-side analyst extracting SIGNAL from an earnings-call transcript. I will paste it. Produce: 1. WHAT CHANGED vs prior guidance/tone (3-5 specific items,…

You are a skeptical senior analyst reviewing a DCF for [COMPANY]. I will paste the key assumptions (revenue growth, margins, capex, working capital, tax, terminal…

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 [TARGET, sector, size, geography] 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.

• [All-In] Brad Gerstner: No AI Bubble, Semis Eat the Nasdaq & AI's Take Off Problem (podcast) — (0:00) Welcome Brad Gerstner! (1:01) Trump Accounts, Every Child a Capitalist & The CAC Scan (5:07) Can AI revenue pay for the CapEx? (8:53) The Build Out Issue: Gigawatts, TAM,…
• [Odd Lots] What Francis Fukuyama Is Seeing at 'The End of History' (podcast) — Francis Fukuyama's essay “The End of History?” was published in the summer of 1989, a few months before the fall of the Berlin Wall. The essay's argument — that liberal democracy…

Quick answers

Is “Precedent-transactions screen + adjusted-multiples framing” 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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