Startup Valuation: A Comprehensive Guide¶

Startup Valuation

A Comprehensive Guide to Valuing Fast-Growing Pre-Revenue Companies

Theory, Methods, Regulation, and Practice

Valuation in Practice Series by Ascent Partners

By Simon Mak

Unlock the secrets of startup valuation with the definitive textbook for finance professionals, investors, founders, and students. Dive deep into probability theory, option pricing, and stochastic processes—explained step-by-step and applied to real startup scenarios.

15 chapters · 5 appendices · 300+ exercises · 20+ real-world cases · 50+ figures · 100+ tables

Buy on Amazon — Kindle $38.80 | Paperback $69.65

Companion Library¶

This site is the companion documentation for the Python library implementing 80+ formulas from the Startup Valuation textbook.

Quick Start¶

pip install -e ".[dev]"

from startup_valuation.core import scorecard_valuation, vc_method_post_money
from startup_valuation.advanced import black_scholes

# Scorecard Method
result = scorecard_valuation(
    average_valuation=1_500_000,
    weights=[0.30, 0.25, 0.15, 0.10, 0.10, 0.05, 0.05],
    scores=[1.25, 1.50, 1.20, 0.75, 1.00, 0.90, 1.00],
)
print(f"Scorecard: ${result.value:,.0f}")  # $1,800,000

# Black-Scholes for real options
result = black_scholes(20_000_000, 5_000_000, 0.05, 0.40, 1.0)
print(f"Option value: ${result.value:,.0f}")  # $15,240,000

Book Chapters → Library Modules¶

Chapter	Topic	Library Module
1	Introduction to Startup Valuation	—
2	Probability, TVM, CAPM	`probability`, `tv`, `capm`
3	Core Valuation Methods	`core`
4	Advanced Methods	`advanced`
5	Comparables & Multiples	`comparables`
6–10	Specialized Topics	See textbook
11	Industry-Specific Models	`saas`, `biotech`, `fintech`, `marketplace`, `hardware`
12	International Valuation	`international`
13	Stakeholder Analysis	`stakeholders`
14	Emerging Methods	`emerging`
15	Conclusion & Best Practices	—

Features¶

80+ valuation formulas across 14 modules covering every method in the textbook
Typed Python API with structured ValuationResult returns (value + assumptions + sensitivity)
MCP Server exposing all calculations as tools for AI agents (Claude, OpenCode, etc.)
AI-Agent Skills with workflow guidance for 5 valuation domains
Every function tested against textbook example values

Modules¶

Module	Methods	Chapter
`probability`	Expected value, joint probability, Poisson	2
`tv`	PV, NPV, annuity	2
`capm`	CAPM, portfolio beta, startup-adjusted	2
`core`	Scorecard, Berkus, Risk Factor, VC Method	3
`advanced`	Black-Scholes, Binomial, Monte Carlo, Scenario	4
`comparables`	Multiples, regression-adjusted	5
`saas`	LTV, CAC, NRR, Magic Number, Rule of 40	11
`biotech`	rNPV, decision tree, peak sales, pipeline	11
`fintech`	Payment revenue, lending, network effects	11
`marketplace`	GMV, take rate, liquidity, network density	11
`hardware`	TRL-adjusted, break-even, P-weighted DCF	11
`international`	PPP, CRP, currency-adjusted DCF	12
`stakeholders`	Dilution, OPM, PWERM, liquidation	13
`emerging`	SAFE, MV=PQ, ESG, Metcalfe's Law	14

MCP Server¶

cd mcp_server && python server.py

Connect with any MCP-compatible AI agent. All 60+ valuation tools are available.

AI-Agent Skills¶

Copy the skills/ directory to your agent's skills folder. Available skills: - valuation-core — Scorecard, Berkus, VC Method, Risk Factor - valuation-advanced — Black-Scholes, Monte Carlo, Scenario Analysis - valuation-industry — SaaS, Biotech, Fintech, Marketplace, Hardware - valuation-stakeholder — Dilution, OPM, PWERM, Liquidation - valuation-emerging — SAFE, Crypto, ESG, Network Effects

Development¶

pip install -e ".[dev]"
pytest --cov=startup_valuation --cov-report=term-missing
ruff check .

License¶

MIT