Excel Templates for Financial Analysis, Forecasting & Valuation
Downloadable .xlsx templates covering DCF and dividend discount valuation, comparable company analysis, sales tracking, AI-assisted forecasting with Copilot and Python in Excel, and sensitivity & scenario analysis. Built for financial analysts, accountants, CA students, and finance professionals.Preview before you download.
AI-Powered Forecasting Template
Pairs a formula-only FORECAST.ETS forecast with a ready-to-paste Python in Excel (=PY) library for trend decomposition, Holt-Winters/ETS, and seasonality charts, plus a Copilot prompt cheat sheet. Companion workbook for the AI Stack Base guide.
Frequently Asked Questions
Are these Excel templates really free to download?
Yes. The DCF valuation model, comparable company analysis, dividend discount model, sales tracker, and sensitivity & scenario analysis templates are all free to download. Only the AI-powered forecasting template is a premium companion workbook.
Do I need Microsoft 365 to use these templates?
Most templates work in any modern version of Excel or LibreOffice. The AI-powered forecasting template is the exception: its formula-only FORECAST.ETS tab works everywhere, but the live Python in Excel (=PY) cells require a Microsoft 365 Current or Beta Channel subscription.
Can I edit and customize these templates for my own company?
Yes. Every template is fully unlocked and editable, so you can rebrand it, change assumptions, add rows, or adapt formulas to fit your own financial statements and reporting needs.
Which template should I use for company valuation?
Use the DCF valuation model for an intrinsic, cash-flow-based valuation, the comparable company analysis for a market-based multiples check, and the dividend discount model when valuing a stable, dividend-paying stock. Many analysts run two or three side by side to cross-check results.
What is the difference between the sensitivity and scenario analysis template and the forecasting template?
The sensitivity & scenario analysis template stress-tests an existing model's assumptions using one-way and two-way tables, Scenario Manager, and Goal Seek. The AI-powered forecasting template instead projects future values from historical data using FORECAST.ETS and Python-based time series methods.
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