DCF Model Builder

Overview

This skill creates institutional-quality DCF models for equity valuation following investment banking standards. Each analysis produces a detailed Excel model (with sensitivity analysis included at the bottom of the DCF sheet).

Tools

Default to using all of the information provided by the user and MCP servers available for data sourcing.

Critical Constraints - Read These First

These constraints apply throughout all DCF model building. Review before starting:

Sensitivity Tables:

Populate ALL 75 cells (3 tables × 25 cells) with full DCF recalculation formulas

Use openpyxl loops to write formulas programmatically

NO placeholder text, NO linear approximations, NO manual steps required

Each cell must recalculate full DCF for that assumption combination

Cell Comments:

Add cell comments AS each hardcoded value is created

Format: "Source: [System/Document], [Date], [Reference], [URL if applicable]"

Every blue input must have a comment before moving to next section

Do not defer to end or write "TODO: add source"

Model Layout Planning:

Define ALL section row positions BEFORE writing any formulas

Write ALL headers and labels first

Write ALL section dividers and blank rows second

THEN write formulas using the locked row positions

Test formulas immediately after creation

Formula Recalculation:

Run

python recalc.py model.xlsx 30

before delivery

Fix ALL errors until status is "success"

Zero formula errors required (#REF!, #DIV/0!, #VALUE!, etc.)

Scenario Blocks:

Create separate blocks for Bear/Base/Bull cases

Show assumptions horizontally across projection years within each block

Use IF formulas:

=IF($B$6=1,[Bear cell],IF($B$6=2,[Base cell],[Bull cell]))

Verify formulas reference correct scenario block cells

DCF Process Workflow

Step 1: Data Retrieval and Validation

Fetch data from MCP servers, user provided data, and the web.

Data Sources Priority:

MCP Servers

(if configured) - Structured financial data from providers like Daloopa

User-Provided Data

- Historical financials from their research

Web Search/Fetch

- Current prices, beta, debt and cash when needed

Validation Checklist:

Verify net debt vs net cash (critical for valuation)

Confirm diluted shares outstanding (check for recent buybacks/issuances)

Validate historical margins are consistent with business model

Cross-check revenue growth rates with industry benchmarks

Verify tax rate is reasonable (typically 21-28%)

Step 2: Historical Analysis (3-5 years)

Analyze and document:

Revenue growth trends

Calculate CAGR, identify drivers

Margin progression

Track gross margin, EBIT margin, FCF margin

Capital intensity

D&A and CapEx as % of revenue

Working capital efficiency

NWC changes as % of revenue growth

Return metrics

ROIC, ROE trends

Create summary tables showing:

Historical Metrics (LTM):

Revenue: $X million

Revenue growth: X% CAGR

Gross margin: X%

EBIT margin: X%

D&A % of revenue: X%

CapEx % of revenue: X%

FCF margin: X%

Step 3: Build Revenue Projections

Methodology:

Start with latest actual revenue (LTM or most recent fiscal year)

Apply growth rates for each projection year

Show both dollar amounts AND calculated growth %

Growth Rate Framework:

Year 1-2: Higher growth reflecting near-term visibility

Year 3-4: Gradual moderation toward industry average

Year 5+: Approaching terminal growth rate

Formula structure:

Revenue(Year N) = Revenue(Year N-1) × (1 + Growth Rate)

Growth %(Year N) = Revenue(Year N) / Revenue(Year N-1) - 1

Three-scenario approach:

Bear Case: Conservative growth (e.g., 8-12%)

Base Case: Most likely scenario (e.g., 12-16%)

Bull Case: Optimistic growth (e.g., 16-20%)

Step 4: Operating Expense Modeling

Fixed/Variable Cost Analysis:

Operating expenses should model realistic operating leverage:

Sales & Marketing

Typically 15-40% of revenue depending on business model

Research & Development

Typically 10-30% for technology companies

General & Administrative

Typically 8-15% of revenue, shows leverage as company scales

Key principles:

ALL percentages based on REVENUE, not gross profit

Model operating leverage: % should decline as revenue scales

Maintain separate line items for S&M, R&D, G&A

Calculate EBIT = Gross Profit - Total OpEx

Margin expansion framework:

Current State → Target State (Year 5)

Gross Margin: X% → Y% (justify based on scale, efficiency)

EBIT Margin: X% → Y% (result of revenue growth + opex leverage)

Step 5: Free Cash Flow Calculation

Build FCF in proper sequence:

EBIT

(-) Taxes (EBIT × Tax Rate)

= NOPAT (Net Operating Profit After Tax)

(+) D&A (non-cash expense, % of revenue)

(-) CapEx (% of revenue, typically 4-8%)

(-) Δ NWC (change in working capital)

= Unlevered Free Cash Flow

Working Capital Modeling:

Calculate as % of revenue change (delta revenue)

Typical range: -2% to +2% of revenue change

Negative number = source of cash (working capital release)

Positive number = use of cash (working capital build)

Maintenance vs Growth CapEx:

Maintenance CapEx: Sustains current operations (~2-3% revenue)

Growth CapEx: Supports expansion (additional 2-5% revenue)

Total CapEx should align with company's growth strategy

Step 6: Cost of Capital (WACC) Research

CAPM Methodology for Cost of Equity:

Cost of Equity = Risk-Free Rate + Beta × Equity Risk Premium

Where:

- Risk-Free Rate = Current 10-Year Treasury Yield

- Beta = 5-year monthly stock beta vs market index

- Equity Risk Premium = 5.0-6.0% (market standard)

Cost of Debt Calculation:

After-Tax Cost of Debt = Pre-Tax Cost of Debt × (1 - Tax Rate)

Determine Pre-Tax Cost of Debt from:

- Credit rating (if available)

- Current yield on company bonds

- Interest expense / Total Debt from financials

Capital Structure Weights:

Market Value Equity = Current Stock Price × Shares Outstanding

Net Debt = Total Debt - Cash & Equivalents

Enterprise Value = Market Cap + Net Debt

Equity Weight = Market Cap / Enterprise Value

Debt Weight = Net Debt / Enterprise Value

WACC = (Cost of Equity × Equity Weight) + (After-Tax Cost of Debt × Debt Weight)

Special Cases:

Net Cash Position

If Cash > Debt, Net Debt is NEGATIVE

Debt Weight may be negative

WACC calculation adjusts accordingly

No Debt

WACC = Cost of Equity

Typical WACC Ranges:

Large Cap, Stable: 7-9%

Growth Companies: 9-12%

High Growth/Risk: 12-15%

Step 7: Discount Rate Application (5-10 Year Forecast)

Mid-Year Convention:

Cash flows assumed to occur mid-year

Discount Period: 0.5, 1.5, 2.5, 3.5, 4.5, etc.

Discount Factor = 1 / (1 + WACC)^Period

Present Value Calculation:

For each projection year:

PV of FCF = Unlevered FCF × Discount Factor

Example (Year 1):

FCF = $1,000

WACC = 10%

Period = 0.5

Discount Factor = 1 / (1.10)^0.5 = 0.9535

PV = $1,000 × 0.9535 = $954

Projection Period Selection:

5 years

Standard for most analyses

7-10 years

High growth companies with longer runway

3 years

Mature, stable businesses

Step 8: Terminal Value Calculation

Perpetuity Growth Method (Preferred):

Terminal FCF = Final Year FCF × (1 + Terminal Growth Rate)

Terminal Value = Terminal FCF / (WACC - Terminal Growth Rate)

Critical Constraint: Terminal Growth < WACC (otherwise infinite value)

Terminal Growth Rate Selection:

Conservative: 2.0-2.5% (GDP growth rate)

Moderate: 2.5-3.5%

Aggressive: 3.5-5.0% (only for market leaders)

Do not exceed

Risk-free rate or long-term GDP growth

Exit Multiple Method (Alternative):

Terminal Value = Final Year EBITDA × Exit Multiple

Where Exit Multiple comes from:

- Industry comparable trading multiples

- Precedent transaction multiples

- Typical range: 8-15x EBITDA

Present Value of Terminal Value:

PV of Terminal Value = Terminal Value / (1 + WACC)^Final Period

Where Final Period accounts for timing:

5-year model with mid-year convention: Period = 4.5

Terminal Value Sanity Check:

Should represent 50-70% of Enterprise Value

If >75%, model may be over-reliant on terminal assumptions

If <40%, check if terminal assumptions are too conservative

Step 9: Enterprise to Equity Value Bridge

Valuation Summary Structure:

(+) Sum of PV of Projected FCFs = $X million

(+) PV of Terminal Value = $Y million

= Enterprise Value = $Z million

(-) Net Debt [or + Net Cash if negative] = $A million

= Equity Value = $B million

÷ Diluted Shares Outstanding = C million shares

= Implied Price per Share = $XX.XX

Current Stock Price = $YY.YY

Implied Return = (Implied Price / Current Price) - 1 = XX%

Critical Adjustments:

Net Debt = Total Debt - Cash & Equivalents

If positive: Subtract from EV (reduces equity value)

If negative (Net Cash): Add to EV (increases equity value)

Use Diluted Shares

Includes options, RSUs, convertible securities

Other adjustments

(if applicable):

Minority interests

Pension liabilities

Operating lease obligations

Valuation Output Format:

Valuation Component

,

Amount ($M)

PV Explicit FCFs

,

X.X

PV Terminal Value

,

Y.Y

Enterprise Value

,

Z.Z

(-) Net Debt

,

A.A

Equity Value

,

B.B

,

,

Shares Outstanding (M)

,

C.C

Implied Price per Share

,

$XX.XX

Current Share Price

,

$YY.YY

Implied Upside/(Downside)

,

+XX%

Step 10: Sensitivity Analysis

Build

three sensitivity tables

at the bottom of the DCF sheet showing how valuation changes with different assumptions:

WACC vs Terminal Growth

- Shows enterprise value sensitivity to discount rate and perpetuity growth

Revenue Growth vs EBIT Margin

- Shows impact of top-line growth and operating leverage

Beta vs Risk-Free Rate

- Shows sensitivity to cost of equity components

Implementation

These are simple 2D grids (NOT Excel's "Data Table" feature) with formulas in each cell. Each cell must contain a full DCF recalculation for that specific assumption combination. See Critical Constraints section for detailed requirements on populating all 75 cells programmatically using openpyxl.

This section contains all the CORRECT patterns to follow when building DCF models.

Scenario Block Selection Pattern - Follow This Approach

Assumptions are organized in separate blocks for each scenario:

CRITICAL STRUCTURE - Three rows per section header:

BEAR CASE ASSUMPTIONS (section header

,

merge cells across)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

12%

,

10%

,

9%

,

8%

,

7%

EBIT Margin (%)

,

45%

,

44%

,

43%

,

42%

,

41%

BASE CASE ASSUMPTIONS (section header

,

merge cells across)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

16%

,

14%

,

12%

,

10%

,

9%

EBIT Margin (%)

,

48%

,

49%

,

50%

,

51%

,

52%

BULL CASE ASSUMPTIONS (section header

,

merge cells across)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

20%

,

18%

,

15%

,

13%

,

11%

EBIT Margin (%)

,

50%

,

51%

,

52%

,

53%

,

54%

Each scenario block MUST have a column header row

showing the projection years (FY2025E, FY2026E, etc.) immediately below the section title. Without this, users cannot tell which assumption value corresponds to which year.

How to reference assumptions - Create a consolidation column:

Case selector cell (e.g., B6) contains 1=Bear, 2=Base, or 3=Bull

Create a consolidation column with INDEX or OFFSET formulas to pull from the correct scenario block

Projection formulas reference the consolidation column (clean cell references)

Each scenario block contains full set of DCF assumptions across projection years

Recommended consolidation column pattern (using INDEX):

=INDEX(B10:D10, 1, $B$6)

NOT this - scattered IF statements throughout:

=IF($B$6=1,[Bear block cell],IF($B$6=2,[Base block cell],[Bull block cell]))

The consolidation column approach centralizes logic and makes the model easier to audit.

Correct Revenue Projection Pattern

Create a consolidation column with INDEX formulas, then reference it in projections:

Step 1 - Consolidation column for FY1 growth:

=INDEX([Bear FY1 growth]:[Bull FY1 growth], 1, $B$6)

Step 2 - Revenue projection references the consolidation column:

Revenue Year 1: =D29*(1+$E$10)

Where:

D29 = Prior year revenue

$E$10 = Consolidation column cell for FY1 growth (contains INDEX formula)

$B$6 = Case selector (1=Bear, 2=Base, 3=Bull)

This approach is cleaner than embedding IF statements in every projection formula

and makes it much easier to audit which scenario assumptions are being used.

Correct FCF Formula Pattern

Use consolidation columns with INDEX formulas, then reference them in FCF calculations:

Consolidation column approach:

Item

,

Formula

,

Reference

D&A

,

=E29*$E$21

,

$E$21 = consolidation column for D&A %

CapEx

,

=E29*$E$22

,

$E$22 = consolidation column for CapEx %

Δ NWC

,

=(E29-D29)*$E$23

,

$E$23 = consolidation column for NWC %

Unlevered FCF

,

=E57+E58-E60-E62

,

E57=NOPAT E58=D&A E60=CapEx E62=Δ NWC

Each consolidation column cell contains an INDEX formula

that pulls from the appropriate scenario block based on case selector. This keeps projection formulas clean and auditable.

Before writing formulas, confirm scenario block row locations and set up consolidation columns.

Correct Cell Comment Format

Every hardcoded value needs this format:

"Source: [System/Document], [Date], [Reference], [URL if applicable]"

Examples:

Item

,

Source Comment

Stock price

,

Source: Market data script 2025-10-12 Close price

Shares outstanding

,

Source: 10-K FY2024 Page 45 Note 12

Historical revenue

,

Source: 10-K FY2024 Page 32 Consolidated Statements

Beta

,

Source: Market data script 2025-10-12 5-year monthly beta

Consensus estimates

,

Source: Management guidance Q3 2024 earnings call

Correct Assumption Table Structure

CRITICAL: Each scenario block requires THREE structural elements:

Section header row

(merged cells): e.g., "BEAR CASE ASSUMPTIONS"

Column header row

showing years - THIS IS REQUIRED, DO NOT SKIP

Data rows

with assumption values

Structure:

BEAR CASE ASSUMPTIONS (section header - merge across columns A:G)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

X%

,

X%

,

X%

,

X%

,

X%

EBIT Margin (%)

,

X%

,

X%

,

X%

,

X%

,

X%

Terminal Growth

,

X%

,

,

,

,

WACC

,

X%

,

,

,

,

BASE CASE ASSUMPTIONS (section header - merge across columns A:G)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

X%

,

X%

,

X%

,

X%

,

X%

EBIT Margin (%)

,

X%

,

X%

,

X%

,

X%

,

X%

Terminal Growth

,

X%

,

,

,

,

WACC

,

X%

,

,

,

,

BULL CASE ASSUMPTIONS (section header - merge across columns A:G)

Assumption

,

FY1

,

FY2

,

FY3

,

FY4

,

FY5

Revenue Growth (%)

,

X%

,

X%

,

X%

,

X%

,

X%

EBIT Margin (%)

,

X%

,

X%

,

X%

,

X%

,

X%

Terminal Growth

,

X%

,

,

,

,

WACC

,

X%

,

,

,

,

WITHOUT the column header row showing projection years (FY2025E, FY2026E, etc.), users cannot tell which assumption value corresponds to which year. This row is MANDATORY.

Then create a consolidation column

(typically the next column to the right) that uses INDEX formulas to pull from the selected scenario block based on the case selector. This consolidation column is what your projection formulas reference.

Correct Row Planning Process

1. Write ALL headers and labels FIRST:

Row

,

Content

1

,

[Company Name] DCF Model

2

,

Ticker | Date | Year End

4

,

Case Selector

7

,

KEY ASSUMPTIONS

26

,

Assumption headers

27-31

,

Growth assumptions

...

,

...

2. Write ALL section dividers and blank rows

3. THEN write formulas using the locked row positions

4. Test formulas immediately after creation

Think of it like construction:

Good: Pour foundation, then build walls (stable structure)

Bad: Build walls, then pour foundation (walls collapse)

Excel version:

Good: Add headers, then write formulas (formulas stable)

Bad: Write formulas, then add headers (formulas break)

Correct Sensitivity Table Implementation

IMPORTANT

These are NOT Excel's "Data Table" feature. These are simple grids where you write regular formulas using openpyxl. Yes, this means ~75 formulas total (3 tables × 25 cells each), but this is straightforward and required. Programmatic Population with Formulas: Each sensitivity table must be fully populated with formulas that recalculate the implied share price for each combination of assumptions. Do not use Excel's Data Table feature (it requires manual intervention and cannot be automated via openpyxl). Implementation approach - CONCRETE EXAMPLE: Table Structure (5x5 grid): WACC vs Terminal Growth , 2.0% , 2.5% , 3.0% , 3.5% , 4.0% 8.0% , [B88 formula] , [C88 formula] , [D88 formula] , [E88 formula] , [F88 formula] 9.0% , [B89 formula] , [C89 formula] , [D89 formula] , [E89 formula] , [F89 formula] ... , ... , ... , ... , ... , ... Formula Pattern - Cell B88 (WACC=8.0%, Terminal Growth=2.0%): The formula in B88 should recalculate the implied price using: WACC from row header: $A88 (8.0%) Terminal Growth from column header: B$87 (2.0%) Recommended approach: Reference the main DCF calculation but substitute these values. Example formula structure: =([SUM of PV FCFs using $A88 as discount rate] + [Terminal Value using B$87 as growth rate and $A88 as WACC] - [Net Debt]) / [Shares] CRITICAL - Write a formula for EVERY cell in the 5x5 grid (25 cells per table, 75 cells total). Use openpyxl to write these formulas programmatically in a loop. Do NOT skip this step or leave placeholder text. Python implementation pattern:

Pseudocode for populating sensitivity table

for row_idx , wacc_value in enumerate ( wacc_range ) : for col_idx , term_growth_value in enumerate ( term_growth_range ) :

Build formula that uses wacc_value and term_growth_value

formula

f"=<DCF recalc using

{

wacc_value

}

and

{

term_growth_value

}

>"

ws

.

cell

(

row

=

start_row

+

row_idx

,

column

=

start_col

+

col_idx

)

.

value

=

formula

The sensitivity tables must work immediately when the model is opened, with no manual steps required from the user.

This section contains all the WRONG patterns to avoid when building DCF models.

WRONG: Simplified Sensitivity Table Approximations or Placeholder Text

Don't use linear approximations:

// WRONG - Linear approximation

B97: =B88*(1+(0.096-0.116)) // Assumes linear relationship

// WRONG - Division shortcut

B105: =B88/(1+(E48-0.07)) // Doesn't recalculate full DCF

Don't leave placeholder text:

// WRONG - Placeholder note

"Note: Use Excel Data Table feature (Data → What-If Analysis → Data Table) to populate sensitivity tables."

// WRONG - Empty cells

[leaving cells blank because "this is complex"]

Don't confuse terminology:

❌ "Sensitivity tables need Excel's Data Table feature" (NO - that's a specific Excel tool we can't use)

✅ "Sensitivity tables are simple grids with formulas in each cell" (YES - this is what we build)

Why these shortcuts are wrong:

Linear approximation formulas don't actually recalculate the DCF - they just apply simple math adjustments

The relationships are not linear, so the results will be inaccurate

Placeholder text requires manual user intervention

Model is not immediately usable when delivered

Not professional or client-ready

Empty cells = incomplete deliverable

Common rationalization to REJECT:

"Writing 75+ formulas feels complex, so I'll leave a note for the user to complete it manually."

Reality:

Writing 75 formulas is straightforward when you use a loop in Python with openpyxl. Each formula follows the same pattern - just substitute the row/column values. This is a required part of the deliverable.

Instead:

Populate every sensitivity cell with formulas that recalculate the full DCF for that specific combination of assumptions

WRONG: Missing Cell Comments

Don't do this:

Create all hardcoded inputs without comments

Think "I'll add them later"

Write "TODO: add source"

Leave blue inputs without documentation

Why it's wrong:

Can't verify where data came from

Fails xlsx skill requirements

Not audit-ready

Wastes time fixing later

Instead:

Add cell comment AS EACH hardcoded value is created

WRONG: Formula Row References Off

Symptom:

The FCF section references wrong assumption rows:

D&A: =E29*$E$34 // Should be $E$21, but referencing wrong row

CapEx: =E29*$E$41 // Should be $E$22, but row shifted

Why this happens:

Formulas written first

Then headers inserted

All row references shifted

Now formulas point to wrong cells → #REF! errors

Instead:

Lock row layout FIRST, then write formulas

WRONG: Single Row for Each Assumption Across Scenarios

Don't structure assumptions like this:

Assumption

,

Bear

,

Base

,

Bull

Revenue Growth FY1

,

10%

,

13%

,

16%

Revenue Growth FY2

,

9%

,

12%

,

15%

This vertical layout makes it hard to see the progression across years within each scenario.

Why it's wrong:

Makes it difficult to see assumptions evolving across years within each scenario

Harder to compare scenario assumptions across full projection period

Less intuitive for reviewing scenario logic

Instead:

Create separate blocks for each scenario (Bear, Base, Bull)

Within each block, show assumptions horizontally across projection years

This makes each scenario's assumptions easier to review as a cohesive set

WRONG: No Borders

Don't deliver a model without borders:

No section delineation

All cells blend together

Hard to read and unprofessional

Why it's wrong:

Not client-ready

Difficult to navigate

Looks amateur

Instead:

Add borders around all major sections

WRONG: Wrong Font Colors or No Font Color Distinction

Don't do this:

All text is black

Only use fill colors (no font color changes)

Mix up which cells are blue vs black

Why it's wrong:

Can't distinguish inputs from formulas

Auditing becomes impossible

Violates xlsx skill requirements

Instead:

Blue text for ALL hardcoded inputs, black text for ALL formulas, green for sheet links

WRONG: Operating Expenses Based on Gross Profit

Don't do this:

S&M: =E33*0.15 // E33 = Gross Profit (WRONG)

Why it's wrong:

Operating expenses scale with revenue, not gross profit

Produces unrealistic margin progression

Not how businesses actually operate

Instead:

S&M: =E29*0.15 // E29 = Revenue (CORRECT)

TOP 5 ERRORS SUMMARY

Formula row references off

→ Define ALL row positions BEFORE writing formulas

Missing cell comments

→ Add comments AS cells are created, not at end

Simplified sensitivity tables

→ Populate all cells with full DCF recalc formulas, not approximations

Scenario block references wrong

→ Ensure IF formulas pull from correct Bear/Base/Bull blocks

No borders

→ Add professional section borders for client-ready appearance

In addition, be aware of these errors:

WACC Calculation Errors

Mixing book and market values in capital structure

Using equity beta instead of asset/unlevered beta incorrectly

Wrong tax rate application to cost of debt

Incorrect risk-free rate (must use current 10Y Treasury)

Failure to adjust for net debt vs net cash position

Growth Assumption Flaws

Terminal growth > WACC (creates infinite value)

Projection growth rates inconsistent with historical performance

Ignoring industry growth constraints

Revenue growth not aligned with unit economics

Margin expansion without operational justification

Terminal Value Mistakes

Using wrong growth method (perpetuity vs exit multiple)

Terminal value >80% of enterprise value (suggests over-reliance)

Inconsistent terminal margins with steady state assumptions

Wrong discount period for terminal value

Cash Flow Projection Errors

Operating expenses based on gross profit instead of revenue

D&A/CapEx percentages misaligned with business model

Working capital changes not properly calculated

Tax rate inconsistency between years

NOPAT calculation errors

These errors are the most common. Re-read this section before starting any DCF build.

Excel File Creation

This skill uses the

xlsx

skill for all spreadsheet operations.

The xlsx skill provides:

Standardized formula construction rules

Number formatting conventions

Automated formula recalculation via

recalc.py

script

Comprehensive error checking and validation

All Excel files created by this skill must follow xlsx skill requirements, including zero formula errors and proper recalculation.

Quality Rubric

Every DCF model must maximize for:

Realistic revenue and margin assumptions

based on historical performance

Appropriate cost of capital calculation

with proper CAPM methodology

Comprehensive sensitivity analysis

showing valuation ranges

Clear terminal value calculation

with supporting rationale

Professional model structure

enabling scenario analysis

Transparent documentation

of all key assumptions

Input Requirements

Minimum Required Inputs

Company identifier

Ticker symbol or company name

Growth assumptions

Revenue growth rates for projection period (or "use consensus")

Optional parameters

:

Projection period (default: 5 years)

Scenario cases (Bear/Base/Bull growth and margin assumptions)

Terminal growth rate (default: 2.5-3.0%)

Specific WACC inputs if not using CAPM

Excel Model Structure

Sheet Architecture

Create

two sheets

:

DCF

- Main valuation model with sensitivity analysis at bottom

WACC

- Cost of capital calculation

CRITICAL

Sensitivity tables go at the BOTTOM of the DCF sheet (not on a separate sheet). This keeps all valuation outputs together.

Formula Recalculation (MANDATORY)

After creating or modifying the Excel model,

recalculate all formulas

using the recalc.py script from the xlsx skill:

python recalc.py

[

path_to_excel_file

]

[

timeout_seconds

]

Example:

python recalc.py AAPL_DCF_Model_2025-10-12.xlsx

30

The script will:

Recalculate all formulas in all sheets using LibreOffice

Scan ALL cells for Excel errors (#REF!, #DIV/0!, #VALUE!, #NAME?, #NULL!, #NUM!, #N/A)

Return detailed JSON with error locations and counts

Expected output format:

{

"status"

:

"success"

,

// or "errors_found"

"total_errors"

:

0

,

// Total error count

"total_formulas"

:

42

,

// Number of formulas in file

"error_summary"

:

{

}

// Only present if errors found

}

If errors are found

, the output will include details:

{

"status"

:

"errors_found"

,

"total_errors"

:

2

,

"total_formulas"

:

42

,

"error_summary"

:

{

"#REF!"

:

{

"count"

:

2

,

"locations"

:

[

"DCF!B25"

,

"DCF!C25"

]

}

}

}

Fix all errors

and re-run recalc.py until status is "success" before delivering the model.

Formatting Standards

IMPORTANT

Follow the xlsx skill for formula construction rules and number formatting conventions. The DCF skill adds specific visual presentation standards.

Color Scheme - Two Layers

:

Layer 1: Font Colors (MANDATORY from xlsx skill)

Blue text (RGB: 0,0,255)

ALL hardcoded inputs (stock price, shares, historical data, assumptions)

Black text (RGB: 0,0,0)

ALL formulas and calculations

Green text (RGB: 0,128,0)

Links to other sheets (WACC sheet references)

Layer 2: Fill Colors (Optional for enhanced presentation)

Fill colors are optional and should only be applied if requested by the user or if enhancing presentation

If the user requests colors or professional formatting, use this standard scheme:

Section headers

Dark blue (RGB: 68,114,196) background with white text

Sub-headers/column headers

Light blue (RGB: 217,225,242) background with black text

Input cells

Light green/cream (RGB: 226,239,218) background with blue text

Calculated cells

White background with black text

Users can override with custom brand colors if specified

How the layers work together (if fill colors are used):

Input cell: Blue text + light green fill = "Hardcoded input"

Formula cell: Black text + white background = "Calculated value"

Sheet link: Green text + white background = "Reference from WACC sheet"

Font color tells you WHAT it is. Fill color tells you WHERE it is (if used).

IMPORTANT:

Font colors from xlsx skill are mandatory. Fill colors are optional - default is white/no fill unless the user requests enhanced formatting or colors.

Border Standards (REQUIRED for Professional Appearance)

Thick borders

(1.5pt) around major sections:

KEY INPUTS section

PROJECTION ASSUMPTIONS section

5-YEAR CASH FLOW PROJECTION section

TERMINAL VALUE section

VALUATION SUMMARY section

Each SENSITIVITY ANALYSIS table

Medium borders

(1pt) between sub-sections:

Company Details vs Historical Performance

Growth Assumptions vs EBIT Margin vs FCF Parameters

Thin borders

(0.5pt) around data tables:

Scenario assumption tables (Bear | Base | Bull | Selected)

Historical vs projected financials matrix

No borders:

Individual cells within tables (keep clean, scannable)

Borders are mandatory

- models without professional borders are not client-ready.

Number Formats

(follows xlsx skill standards):

Years

Format as text strings (e.g., "2024" not "2,024")

Percentages

:

0.0%

(one decimal place)

Currency

:

$#,##0

for millions;

$#,##0.00

for per-share - ALWAYS specify units in headers ("Revenue ($mm)")

Zeros

Use number formatting to make all zeros "-" (e.g., $#,##0;($#,##0);- ) Large numbers :

,##0

with thousands separator

Negative numbers

:

(#,##0)

in parentheses (NOT minus sign)

Cell Comments (MANDATORY for all hardcoded inputs)

:

Per the xlsx skill, ALL hardcoded values must have cell comments documenting the source. Format: "Source: [System/Document], [Date], [Reference], [URL if applicable]"

CRITICAL

Add comments AS CELLS ARE CREATED. Do not defer to the end.

DCF Sheet Detailed Structure

Section 1: Header

Row

,

Content

1

,

[Company Name] DCF Model

2

,

Ticker: [XXX] | Date: [Date] | Year End: [FYE]

3

,

Blank

4

,

Case Selector Cell (1=Bear 2=Base 3=Bull)

5

,

Case Name Display (formula: =IF([Selector]=1

"Bear"

IF([Selector]=2

"Base""Bull"

)))

Section 2: Market Data (NOT case dependent)

Item

,

Value

Current Stock Price

,

$XX.XX

Shares Outstanding (M)

,

XX.X

Market Cap ($M)

,

[Formula]

Net Debt ($M)

,

XXX [or Net Cash if negative]

Section 3: DCF Scenario Assumptions

Create separate assumption blocks for each scenario (Bear, Base, Bull) with DCF-specific assumptions (Revenue Growth %, EBIT Margin %, Tax Rate %, D&A % of Revenue, CapEx % of Revenue, NWC Change % of ΔRev, Terminal Growth Rate, WACC) laid out horizontally across projection years. Each block must include section header, column header row showing the projection years (FY1, FY2, etc.), and data rows. See

section "Correct Assumption Table Structure" for the exact layout.

Section 4: Historical & Projected Financials

Reference a consolidation column (e.g., "Selected Case") that pulls from scenario blocks

, not scattered IF formulas in every projection row.

Income Statement ($M)

,

2020A

,

2021A

,

2022A

,

2023A

,

2024E

,

2025E

,

2026E

Revenue

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*(1+$E$10)]

,

[=F29*(1+$E$11)]

,

[=G29*(1+$E$12)]

% growth

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E29/D29-1]

,

[=F29/E29-1]

,

[=G29/F29-1]

,

,

,

,

,

,

Gross Profit

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*E33]

,

[=F29*F33]

,

[=G29*G33]

% margin

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E33/E29]

,

[=F33/F29]

,

[=G33/G29]

,

,

,

,

,

,

Operating Expenses:

,

,

,

,

,

,

,

S&M

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*0.15]

,

[=F29*0.14]

,

[=G29*0.13]

R&D

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*0.12]

,

[=F29*0.11]

,

[=G29*0.10]

G&A

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*0.08]

,

[=F29*0.07]

,

[=G29*0.07]

Total OpEx

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E36+E37+E38]

,

[=F36+F37+F38]

,

[=G36+G37+G38]

,

,

,

,

,

,

EBIT

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E33-E39]

,

[=F33-F39]

,

[=G33-G39]

% margin

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E41/E29]

,

[=F41/F29]

,

[=G41/G29]

,

,

,

,

,

,

Taxes

,

(XX)

,

(XX)

,

(XX)

,

(XX)

,

[=E41*$E$24]

,

[=F41*$E$24]

,

[=G41*$E$24]

Tax rate

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E43/E41]

,

[=F43/F41]

,

[=G43/G41]

,

,

,

,

,

,

NOPAT

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E41-E43]

,

[=F41-F43]

,

[=G41-G43]

Key Formula Pattern

:

Revenue growth:

=E29*(1+$E$10)

where $E$10 is consolidation column for Year 1 growth

NOT:

=E29*(1+IF($B$6=1,$B$10,IF($B$6=2,$C$10,$D$10)))

This approach is cleaner, easier to audit, and prevents formula errors by centralizing the scenario logic.

Section 5: Free Cash Flow Build

CRITICAL

Verify row references point to the CORRECT assumption rows. Test formulas immediately after creation.

Cash Flow ($M)

,

2020A

,

2021A

,

2022A

,

2023A

,

2024E

,

2025E

,

2026E

NOPAT

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E45]

,

[=F45]

,

[=G45]

(+) D&A

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E29*$E$21]

,

[=F29*$E$21]

,

[=G29*$E$21]

% of Rev

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E58/E29]

,

[=F58/F29]

,

[=G58/G29]

(-) CapEx

,

(XX)

,

(XX)

,

(XX)

,

(XX)

,

[=E29*$E$22]

,

[=F29*$E$22]

,

[=G29*$E$22]

% of Rev

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E60/E29]

,

[=F60/F29]

,

[=G60/G29]

(-) Δ NWC

,

(XX)

,

(XX)

,

(XX)

,

(XX)

,

[=(E29-D29)*$E$23]

,

[=(F29-E29)*$E$23]

,

[=(G29-F29)*$E$23]

% of Δ Rev

,

XX%

,

XX%

,

XX%

,

XX%

,

[=E62/(E29-D29)]

,

[=F62/(F29-E29)]

,

[=G62/(G29-F29)]

,

,

,

,

,

,

Unlevered FCF

,

XXX

,

XXX

,

XXX

,

XXX

,

[=E57+E58-E60-E62]

,

[=F57+F58-F60-F62]

,

[=G57+G58-G60-G62]

Row reference examples

(based on layout planning):

$E$21 = D&A % assumption (consolidation column, row 21)

$E$22 = CapEx % assumption (consolidation column, row 22)

$E$23 = NWC % assumption (consolidation column, row 23)

E29 = Revenue for year (row 29)

E45 = NOPAT for year (row 45)

Before writing formulas

Confirm these row numbers match the actual layout. Test one column, then copy across.

Section 6: Discounting & Valuation

DCF Valuation

,

2024E

,

2025E

,

2026E

,

2027E

,

2028E

,

Terminal

Unlevered FCF ($M)

,

XXX

,

XXX

,

XXX

,

XXX

,

XXX

,

Period

,

0.5

,

1.5

,

2.5

,

3.5

,

4.5

,

Discount Factor

,

0.XX

,

0.XX

,

0.XX

,

0.XX

,

0.XX

,

PV of FCF ($M)

,

XXX

,

XXX

,

XXX

,

XXX

,

XXX

,

,

,

,

,

,

,

Terminal FCF ($M)

,

,

,

,

,

,

,

XXX

Terminal Value ($M)

,

,

,

,

,

,

,

XXX

PV Terminal Value ($M)

,

,

,

,

,

,

,

XXX

,

,

,

,

,

,

Valuation Summary ($M)

,

,

,

,

,

,

Sum of PV FCFs

,

XXX

,

,

,

,

,

PV Terminal Value

,

XXX

,

,

,

,

,

Enterprise Value

,

XXX

,

,

,

,

,

(-) Net Debt

,

(XX)

,

,

,

,

,

Equity Value

,

XXX

,

,

,

,

,

,

,

,

,

,

,

Shares Outstanding (M)

,

XX.X

,

,

,

,

,

IMPLIED PRICE PER SHARE

,

$XX.XX

,

,

,

,

,

Current Stock Price

,

$XX.XX

,

,

,

,

,

Implied Upside/(Downside)

,

XX%

,

,

,

,

,

WACC Sheet Structure

COST OF EQUITY CALCULATION

,

,

Risk-Free Rate (10Y Treasury)

,

X.XX%

,

[Yellow input]

Beta (5Y monthly)

,

X.XX

,

[Yellow input]

Equity Risk Premium

,

X.XX%

,

[Yellow input]

Cost of Equity

,

X.XX%

,

[Calculated blue]

,

,

COST OF DEBT CALCULATION

,

,

Credit Rating

,

AA-

,

[Yellow input]

Pre-Tax Cost of Debt

,

X.XX%

,

[Yellow input]

Tax Rate

,

XX.X%

,

[Link to DCF sheet]

After-Tax Cost of Debt

,

X.XX%

,

[Calculated blue]

,

,

CAPITAL STRUCTURE

,

,

Current Stock Price

,

$XX.XX

,

[Link to DCF]

Shares Outstanding (M)

,

XX.X

,

[Link to DCF]

Market Capitalization ($M)

,

"X,XXX"

,

[Calculated]

,

,

Total Debt ($M)

,

XXX

,

[Yellow input]

Cash & Equivalents ($M)

,

XXX

,

[Yellow input]

Net Debt ($M)

,

XXX

,

[Calculated]

,

,

Enterprise Value ($M)

,

"X,XXX"

,

[Calculated]

,

,

WACC CALCULATION

,

Weight

,

Cost

,

Contribution

Equity

,

XX.X%

,

X.X%

,

X.XX%

Debt

,

XX.X%

,

X.X%

,

X.XX%

,

,

WEIGHTED AVERAGE COST OF CAPITAL

,

X.XX%

,

[Green output]

Key WACC Formulas:

Market Cap = Price × Shares

Net Debt = Total Debt - Cash

Enterprise Value = Market Cap + Net Debt

Equity Weight = Market Cap / EV

Debt Weight = Net Debt / EV

WACC = (Cost of Equity × Equity Weight) + (After-tax Cost of Debt × Debt Weight)

Sensitivity Analysis (Bottom of DCF Sheet)

TERMINOLOGY REMINDER

"Sensitivity tables" = simple 2D grids with row headers, column headers, and formulas in each data cell. NOT Excel's "Data Table" feature (Data → What-If Analysis → Data Table). You will use openpyxl to write regular Excel formulas into each cell.

Location

Rows 87+ on DCF sheet (NOT a separate sheet)

Three sensitivity tables, vertically stacked:

WACC vs Terminal Growth

(rows 87-100) - 5x5 grid = 25 cells with formulas

Revenue Growth vs EBIT Margin

(rows 102-115) - 5x5 grid = 25 cells with formulas

Beta vs Risk-Free Rate

(rows 117-130) - 5x5 grid = 25 cells with formulas

Total formulas to write: 75

(this is required, not optional)

CRITICAL

All sensitivity table cells must be populated programmatically with formulas using openpyxl. DO NOT use linear approximation shortcuts. DO NOT leave placeholder text or notes about manual steps. DO NOT rationalize leaving cells empty because "it's complex" - use a Python loop to generate the formulas.

Table Setup:

Create table structure with row/column headers (the assumption values to test)

Populate EVERY data cell with a formula that:

Uses the row header value (e.g., WACC = 9.0%)

Uses the column header value (e.g., Terminal Growth = 3.0%)

Recalculates the full DCF with those specific assumptions

Returns the implied share price for that scenario

All cells must contain working formulas when delivered

Format cells with conditional formatting: Green scale for higher values, red scale for lower values

Bold the base case cell

Leave 1-2 blank rows between tables

No manual intervention required

- the sensitivity tables must be fully functional when the user opens the file.

Case Selector Implementation

Three-Case Framework:

Bear Case

Conservative revenue growth (low end of historical range)

Margin compression or no expansion

Higher WACC (risk premium increase)

Lower terminal growth rate

Higher CapEx assumptions

Base Case

Consensus or management guidance revenue growth

Moderate margin expansion based on operating leverage

Current market-implied WACC

GDP-aligned terminal growth (2.5-3.0%)

Standard CapEx assumptions

Bull Case

Optimistic revenue growth (high end of projections)

Significant margin expansion

Lower WACC (reduced risk premium)

Higher terminal growth (3.5-5.0%)

Reduced CapEx intensity

Formula Implementation:

DO NOT use nested IF formulas scattered throughout.

Instead, create a consolidation column that uses INDEX or OFFSET formulas to pull from the appropriate scenario block.

Recommended pattern (using INDEX):

=INDEX(B10:D10, 1, $B$6)

where

B10:D10

= Bear/Base/Bull values,

1

= row offset,

$B$6

= case selector cell (1, 2, or 3)

Then reference the consolidation column

in all projections:

Revenue Year 1: =D29*(1+$E$10)

where $E$10 is the consolidation column value for Year 1 growth.

This approach centralizes scenario logic, making the model easier to audit and maintain.

Deliverables Structure

File naming

:

[Ticker]DCF_Model[Date].xlsx

Two sheets

:

DCF

- Complete model with Bear/Base/Bull cases + three sensitivity tables at bottom (WACC vs Terminal Growth, Revenue Growth vs EBIT Margin, Beta vs Risk-Free Rate)

WACC

- Cost of capital calculation

Key features

Case selector (1/2/3), consolidation column with INDEX/OFFSET formulas, color-coded cells, cell comments on all inputs, professional borders

Best Practices

Model Construction

Build incrementally

Complete each section before moving to next

Test as building

Enter sample numbers to verify formulas

Use consistent structure

Similar calculations follow similar patterns

Comment complex formulas

Add notes for unusual calculations

Build in checks

Sum checks and balance checks where applicable

Documentation

Document all assumptions

Explain reasoning behind key inputs

Cite data sources

Note where each data point came from

Explain methodology

Describe any non-standard approaches

Flag uncertainties

Highlight areas with limited visibility

Quality Control

Cross-check calculations

Verify math in multiple ways

Stress test assumptions

Run sensitivity to ensure model is robust

Peer review

Have someone else check formulas

Version control

Save versions as work progresses

Common Variations

High-Growth Technology Companies

Longer projection period (7-10 years)

Higher initial growth rates (20-30%)

Significant margin expansion over time

Higher WACC (12-15%)

Model unit economics (users, ARPU, etc.)

Mature/Stable Companies

Shorter projection period (3-5 years)

Modest growth rates (GDP +1-3%)

Stable margins

Lower WACC (7-9%)

Focus on cash generation and capital allocation

Cyclical Companies

Model through economic cycle

Normalize margins at mid-cycle

Consider trough and peak scenarios

Adjust beta for cyclicality

Multi-Segment Companies

Separate DCFs for each business unit

Different growth rates and margins by segment

Sum-of-parts valuation

Consider synergies

Troubleshooting

If you encounter errors or unreasonable results, read

TROUBLESHOOTING.md

for detailed debugging guidance.

Workflow Integration

At Start of DCF Build

Gather market data

:

Check for available MCP servers for current market data

Use web search/fetch for stock prices, beta, and other market metrics

Request from user if specific data is needed

Gather historical financials

:

Check for available MCP servers (Daloopa, etc.)

Request from user if not available via MCP

Manual extraction from 10-Ks if necessary

Begin model construction

using the DCF methodology detailed in this skill

During Model Construction

Build Excel model

using openpyxl with formulas (not hardcoded values)

Follow xlsx skill conventions

for formula construction and formatting

Apply fill colors only if requested

by user or if specific brand guidelines are provided

Before Delivering Model (MANDATORY)

Verify structure

:

Scenario blocks for Bear/Base/Bull with assumptions across projection years

Case selector functional with formulas referencing correct scenario blocks

Sensitivity tables at bottom of DCF sheet (not separate sheet)

Font colors: Blue inputs, black formulas, green sheet links

Cell comments on ALL hardcoded inputs

Professional borders around major sections

Recalculate formulas

Run

python recalc.py model.xlsx 30

Check output

:

If

status

is

"success"

→ Continue to step 4

If

status

is

"errors_found"

→ Check

error_summary

and read

TROUBLESHOOTING.md

for debugging guidance

Fix errors and re-run recalc.py

until status is "success"

Spot-check formulas

:

Test one FCF formula - does it reference the correct assumption rows?

Change case selector - does the consolidation column update properly?

Verify revenue formulas reference consolidation column (not nested IF formulas)

Deliver model

Available Data Sources

MCP servers

If configured (Daloopa for historical financials)

Web search/fetch

For current stock prices, beta, and market data

User-provided data

Historical financials, consensus estimates

Manual extraction

SEC EDGAR filings as fallback Final Output Checklist Before delivering DCF model: Required: Run python recalc.py model.xlsx 30 until status is "success" (zero formula errors) Two sheets: DCF (with sensitivity at bottom), WACC Font colors: Blue=inputs, Black=formulas, Green=sheet links Cell comments on ALL hardcoded inputs Sensitivity tables fully populated with formulas Professional borders around major sections Validation: OpEx based on revenue (not gross profit) Terminal value 50-70% of EV Terminal growth < WACC Tax rate 21-28% File naming: [Ticker]DCF_Model[Date].xlsx