> ## Documentation Index
> Fetch the complete documentation index at: https://valuation-101.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Growth & Profitability

> Set the four forecast variables that drive the 10-year DCF projection — each with a start value, end value, and convergence curve.

## What it does

Collects and validates the four forecast variables that drive the 10-year FCFF projection. For each variable, the skill determines a **start value** (year 1), an **end value** (year 10 target), and a **convergence curve type** that controls the transition shape. The [curve shapes library](/docs/lib/curve-shapes) then generates a full 10-year schedule that is passed directly to the DCF engine.

## The four forecast variables

| Variable                   | Start value (Year 1)                   | End value (Year 10)                           | What it controls                           |
| -------------------------- | -------------------------------------- | --------------------------------------------- | ------------------------------------------ |
| **Revenue growth**         | LTM growth rate (or earnings guidance) | Industry 5-year average growth                | Top-line trajectory and deceleration shape |
| **Operating margin**       | LTM EBIT margin (or earnings guidance) | Industry average (or guided long-term target) | Profitability convergence path             |
| **Sales-to-capital ratio** | Company's current ratio                | Industry average                              | Reinvestment efficiency evolution          |
| **Cost of capital**        | Initial WACC from Phase 5              | Risk-free rate + base ERP                     | Risk premium decline as company matures    |

Each variable transitions from start to end over 10 years using one of [six convergence curve types](/docs/lib/curve-shapes):

| Curve                | Shape                             | Typical use                                              |
| -------------------- | --------------------------------- | -------------------------------------------------------- |
| Exponential Decay    | Fast initial change, gradual tail | Revenue growth decelerating as market saturates          |
| Standard S-Curve     | Flat → rapid → flat               | Margins expanding after moat-protected growth phase      |
| Rapid Deceleration   | Very fast convergence             | Large gap between current and target, fast normalization |
| Linear               | Constant rate of change           | Steady, predictable transitions                          |
| Delayed Deceleration | Holds near start, then drops      | Strong near-term visibility (backlog-driven)             |
| Step-Down            | Flat then discrete jump           | Margin cliffs (patent expiry, contract loss)             |

## How schedules interact

The four 10-year schedules together determine every line in the projection:

* **Revenue(t)** = Revenue(t−1) × (1 + growth\_schedule\[t])
* **EBIT(t)** = Revenue(t) × margin\_schedule\[t]
* **Reinvestment(t)** = ΔRevenue / s2c\_schedule\[t]
* **FCFF(t)** = EBIT(t) × (1 − tax\_rate) − Reinvestment(t)
* **PV(FCFF)** = FCFF(t) / (1 + coc\_schedule\[t])^t

## Earnings guidance integration

When [earnings guidance](/docs/skills/earnings-guidance) is available from Phase 5.5, it overrides LTM-derived defaults:

| Variable                 | With guidance                             | Without guidance   |
| ------------------------ | ----------------------------------------- | ------------------ |
| Revenue growth — start   | Implied growth from guided dollar revenue | LTM growth rate    |
| Operating margin — start | Guided year 1 margin                      | LTM EBIT / Revenue |
| Operating margin — end   | Guided long-term target (if given)        | Industry average   |

In **Expert mode**, guided values are presented alongside industry benchmarks — the user can accept, modify, or reject each one. In **Lucky mode**, guidance is applied automatically.

## Industry benchmarks

For each variable, the skill shows how the company compares to its industry:

| Metric                   | Company (LTM) | US Avg | Global Avg | Q1 | Median | Q3 |
| ------------------------ | ------------- | ------ | ---------- | -- | ------ | -- |
| Revenue growth           | ✓             | ✓      | ✓          | ✓  | ✓      | ✓  |
| Pre-tax operating margin | ✓             | ✓      | ✓          | ✓  | ✓      | ✓  |
| Sales-to-capital ratio   | ✓             | ✓      | ✓          | ✓  | ✓      | ✓  |
| ROIC                     | ✓             | ✓      | ✓          | —  | —      | —  |

Sources: Damodaran Jan 2026 industry averages (US and Global), Input Stat Distributions (Global). Company actuals computed from LTM SEC filings.

## Mode behavior

| Mode              | How variables are set                                                                                                                                                               |
| ----------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Expert**        | Full benchmark table shown, then all four variables presented with start/end/curve defaults. User overrides any values or curve types.                                              |
| **Novice**        | Two story questions: "How fast will this company grow?" and "How profitable will it be?" Responses are mapped to the four variables using industry quartiles. Curves auto-selected. |
| **Feeling Lucky** | Fully automatic — start values from LTM or guidance, end values from industry targets, curves auto-classified. Zero questions asked.                                                |

## Curve auto-classification

In Feeling Lucky and Novice modes, curve types are selected automatically based on:

* **Direction**: ascending (margin expansion) vs. descending (growth deceleration)
* **Gap magnitude**: how far the start value is from the target
* **Company trajectory**: recent trend direction and velocity
* **Industry context**: whether the company is far outside industry norms

For example, a company with 168% LTM revenue growth converging to a 29% industry average would get an **Exponential Decay** curve, while a company with very low sales-to-capital ratio (0.17x vs. 1.35x industry) would get an **S-Curve** to model gradual efficiency improvement.

## Validation

After determining the schedules (in any mode), the skill flags unusual values: very high year 1 growth rates, negative target margins, very low or very high sales-to-capital ratios, or curves that imply implausible trajectories. In Expert mode, all flags require confirmation. In Lucky mode, flags are noted but the pipeline proceeds.

## Output

The skill saves `growth_assumptions.json` to the run directory, containing:

* The four 10-year schedules (`growth_schedule`, `margin_schedule`, `s2c_schedule`, `coc_schedule`)
* Curve type selections and metadata
* Start/end values with source annotations (LTM, guidance, industry)
* Industry benchmarks for comparison
* Legacy scalar fields maintained for backward compatibility