target Chapter 25 — Goal-Seek Solver

Goal-Seek Solver

Reverse-Engineer Your Target IRR or Transaction Price

The Goal-Seek Solver inverts the DCF: instead of computing value from assumptions, it finds the assumption — terminal growth rate, exit multiple, or revenue growth rate — that must be true for a given transaction price to be fair, or for a target IRR to be achievable. The essential tool for M&A pricing validation, investor return analysis, and negotiation preparation.

Ch. 25
Report Chapter
3
Solvable Variables
IRR
Primary Target Metric
Newton
Iteration Algorithm

What Is the Goal-Seek Solver?

A standard DCF model works forward: given assumptions about growth, margins, WACC, and terminal value, it computes an Enterprise Value. The Goal-Seek Solver works backward: given a known or target Enterprise Value (or Equity Value, or entry price), it solves for whichever assumption makes the model balance — the implied terminal growth rate, the required exit multiple, or the revenue growth rate that must be achieved.

This reverse-engineering capability is uniquely powerful in three contexts: (1) M&A validation — when an acquisition price is known, the Goal-Seek Solver reveals the growth rate or exit multiple baked into that price, letting the acquirer assess whether those implied assumptions are realistic; (2) investor return analysis — given a fund's target IRR and the proposed entry valuation, the solver finds the exit multiple or growth path required to achieve that return; (3) negotiation preparation — a seller can determine what assumptions a buyer would need to hold to justify a higher bid, providing a rational basis for price negotiations.

The Goal-Seek Logic

STANDARD DCF (forward):
f(assumptions) → Enterprise Value
GOAL-SEEK (inverse):
f−1(Target EV or Target IRR) → Required Assumption
Target Metric = Entry price, Equity Value, or IRR
Solved Variable = Terminal growth rate, exit multiple, or revenue CAGR
Algorithm = Newton-Raphson iterative numerical solver
Convergence = Tolerance < 0.01% of target value

How Equitest Implements the Goal-Seek Solver

Chapter 25 is a one-click solver built directly on the DCF engine — requiring no spreadsheet, no manual iteration, and no separate model.

Ch. 25 — Solver Mode 1

Solve for Terminal Growth Rate

Given a known transaction price or target IRR, the solver finds the terminal growth rate that makes the DCF model produce exactly that value — holding all other assumptions fixed. The result is presented as the "implied terminal growth rate" of the transaction price. If this implied rate is above GDP growth, the price implies expectations that cannot be sustained in perpetuity.

Ch. 25 — Solver Mode 2

Solve for Exit Multiple

The solver finds the exit EBITDA multiple in the terminal year that produces the target value — revealing whether the price is supported by realistic exit multiples. The implied exit multiple is then benchmarked against the observable range of current market multiples for comparable companies, providing an immediate market reality check on the transaction price.

Ch. 25 — Solver Mode 3

Solve for Required Revenue CAGR

Given a target IRR and an entry price, the solver finds the minimum revenue compound annual growth rate required across the explicit projection period for the investment to meet its return hurdle. This is the essential input for investor due diligence: does the required growth rate fall within the company's historical range, industry norms, and the analyst's projection assumptions?

Ch. 25 — IRR Mode

Entry Price to IRR — Direct Computation

When the entry price, projection period, and exit assumptions are all known, Chapter 25 computes the implied IRR directly — showing the investor's expected return under base, upside, and downside growth scenarios. The IRR output is displayed alongside the fund's hurdle rate, if entered, giving an immediate pass/fail signal on the investment thesis.

Ch. 25 — Solver Algorithm

Newton-Raphson Iteration with Convergence Guarantee

The solver uses the Newton-Raphson iterative numerical method — the standard algorithm for this class of problem — with a convergence tolerance of <0.01% of the target value. Starting from the current model assumption as the initial estimate, it converges in milliseconds for standard valuation ranges. If no solution exists (e.g., the target value is not achievable under any positive growth rate), the system reports this explicitly rather than returning a nonsense result.

Ch. 25 — Report Output

Solved Assumption with Plausibility Assessment

The Chapter 25 report section presents the solved assumption alongside an auto-generated plausibility assessment: how does the implied terminal growth rate compare to GDP? How does the implied exit multiple compare to the current observable range? How does the required revenue CAGR compare to the company's historical growth rate and industry benchmarks? This contextual framing transforms a raw numerical result into an actionable analytical conclusion.

Reverse-Engineer Any Transaction Price

Implied terminal growth rate. Required exit multiple. Minimum revenue CAGR. IRR from entry to exit. All solved in one click from Chapter 25.