ChatGPT Prompts for Investment Banking: 12 Analyst Prompts

ChatGPT prompts for investment banking save analysts time on structure and first drafts: laying out a comps table from data you paste, outlining a pitch book, summarizing a CIM, drafting a deal memo, and checking a model for errors. They do not replace FactSet, Capital IQ, or the filings, and ChatGPT should never be the source of a price, multiple, or financial figure. Below are 12 copy-paste prompts grouped by deal task, plus a flagship comps prompt that shows every calculation.

A note on compliance: never paste material non-public information, client names under NDA, or confidential deal data into a public AI tool. Use your firm's approved tools and policies. These prompts support analysis; they are not investment advice, and every number must be checked against the source.

Prompts for valuation work

1. Comps table from pasted data

Here is peer data as of [date]: [price, diluted shares, debt, cash, LTM revenue, LTM EBITDA per peer]. Build a comps table with enterprise value, EV/Revenue, and EV/EBITDA. Show the formula, flag outliers with a reason, and give low, median, mean, and high.

2. Peer set screen

Here is a list of possible peers with revenue, growth, margin, and business mix: [list]. Score each against our target, [target description], on size, business model, and end market. Recommend a core set and say who is least comparable and why. Use only my data.

3. Precedent transactions summary

Here are precedent deals I pulled: [date, acquirer, target, EV, revenue, EBITDA]. Calculate EV/Revenue and EV/EBITDA for each, note deals with missing data, and summarize the range with and without the oldest deals.

4. Football field inputs

From these valuation ranges, [comps range, precedents range, DCF range, 52-week trading range], build the table behind a football field chart: method, low, high, and midpoint. Check that units match.

Prompts for pitch books and documents

5. Pitch book outline

Outline a pitch book for [situation, such as sell-side for a mid-market software company] for [client type]. List each section and page, the one message per page, and which data I need to supply for it.

6. CIM summary

Here is a section of a CIM: [text]. Summarize the business, revenue model, customers, growth drivers, and risks in one page of bullets. Quote figures exactly and mark anything that is a management projection.

7. Teaser draft

Write a one-page anonymous teaser for a company with these facts: [industry, size range, highlights we may share]. Do not include anything that could identify the company, such as city, founding year, or exact revenue, unless I mark it as cleared.

8. Deal memo first draft

Draft an internal deal memo from these notes: [situation, valuation summary, key risks, recommendation]. Sections: summary, company, valuation, risks, next steps. Use only my notes and leave [TBD] for any gap.

Prompts for models and checks

9. Model error check

Here are formulas and outputs from my model: [paste]. Look for broken links, hard-coded numbers inside formulas, sign errors, and balance sheet items that do not balance. List each issue with the cell reference.

10. Sensitivity table explanation

Here is my sensitivity table of [output] across [variable 1] and [variable 2]: [table]. Explain in three bullets what drives the value most and which cases look unrealistic given [my assumptions].

Prompts for clients and the team

11. Client call prep

I have a call with [client role] about [topic]. From my notes, [notes], list the five questions they are likely to ask, short answers from my notes, and the data I should have open.

12. Comments to action list

Here are my MD's comments on the deck: [comments]. Turn them into an action list by page with owner, change needed, and data required, and flag comments that conflict with each other.

Build a checked comps table with the flagship prompt

The Investment Banking Comps Table from Pasted Company Data prompt expands prompt 1. In its example, five peers are pasted with price, diluted shares, debt, cash, and LTM figures. The prompt computes every enterprise value, flags one peer whose 13% EBITDA margin pushes its EV/EBITDA to 51.0x, shows statistics with and without it, and applies the middle peers and median to a private target to give implied enterprise and equity values, with open questions for the deal team.

Related prompts

For spreadsheet work around a model, see ChatGPT Prompts for Excel Data Analysis. For close and reporting tasks, see ChatGPT Prompts for Accountants, and for client-facing wealth work, ChatGPT Prompts for Financial Advisors.

Find more prompts in the Finance category, look through Browse, or post your own on Share.