PromptSharp › Daily briefs › Finance › August 5, 2026

Finance prompt of the day

August 5, 2026 · for Investment banking, sales & trading, equity research, FP&A. One sharp, copy-paste prompt — free, every weekday.

You are the smartest short-seller on the other side of my long in [TICKER]. I will give the thesis, entry, and sizing. Produce:
1. The 3 strongest reasons this long is WRONG, each with the mechanism, not just an assertion.
2. A TABLE of what the market may already know that I'm discounting (crowding, consensus, sell-side rating skew, positioning).
3. The base-rate/outside view: how often do setups like this work, and what's my edge over that base rate?
4. The single fact that, if true, ends the thesis — and how I'd check it.
Do not strawman the bear case; make it genuinely hard. Flag every claim I should verify.

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 “Long thesis pre-mortem”, step by step

The situation this prompt is built for: Before you size up a long, you want the sharpest bear case and the falsification levels. 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. The 3 strongest reasons this long is WRONG, each with the mechanism, not just an assertion.
2. A TABLE of what the market may already know that I'm discounting (crowding, consensus, sell-side rating skew, positioning).
3. The base-rate/outside view: how often do setups like this work, and what's my edge over that base rate?
4. The single fact that, if true, ends the thesis — and how I'd check it.

Why this structure works

A structured pre-mortem plus an outside-view base rate is how disciplined PMs avoid confirmation bias and marrying a position; the 'single fact that ends it' gives you a concrete falsification test.

On Pro, the bear-case framing and base-rate references are tuned to your strategy and the failure modes your book is most exposed to, from your saved profile.

When to use it — and when not to

Reach for it when

• Before you size up a long, you want the sharpest bear case and the falsification levels.
• 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

“Long thesis pre-mortem” 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 a deal-team associate. For a sell-side M&A process on [TARGET, expected timeline in months], produce: 1. A phased TIMELINE (Preparation, Marketing, Management…

You are a derivatives salesperson explaining an options structure to a client. The structure is [describe: e.g. call spread, risk reversal, collar, put spread] on…

You are an analyst reviewing the unit economics of [COMPANY / business model]. I will give the drivers (CAC, LTV, gross margin, churn/retention, payback, cohort data).…

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.

• Google Earnings, The Frontier Case, Amazon Earnings (rss) — Google's earnings seemed to confirm the Anthropic hedge; it was Andy Jassy who explained why their — and Amazon's — capex was justifiable.
• BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences? (arxiv) — Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reasoning tasks, there is…

Quick answers

Is “Long thesis pre-mortem” 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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