MBB Investment Committee Modeling Tool

Problem Addressed

Investment committee modeling often gets stuck on concrete issues: whether financial statement fields are usable, whether metric definitions are consistent, whether DCF output is far from comparable multiples, whether budget variances can be explained, and how long a single project takes to recover investment. This skill turns those questions into a structured financial analysis workflow rather than a generic template. It is useful for pre-decision checks on ratios, DCF valuation, budget variance, and single-project payback.

How It Works

The skill follows a five-phase flow: Scoping sets objectives, data periods, and materiality thresholds; Data Analysis & Modeling validates completeness of income statement, balance sheet, and cash flow, then computes financial ratios across five categories. It then builds WACC, terminal value, sensitivity analysis, and driver-based forecasts with base/bull/bear scenarios.

Core capabilities include:
- Ratio calculation: profitability, liquidity, leverage, efficiency, and valuation metrics such as ROE, Debt-to-Equity, and EV/EBITDA.
- DCF valuation: a five-year default projection, perpetuity growth and exit multiple terminal value, and two-way sensitivity.
- Budget variance: department and category breakdown with favorable/unfavorable classification, using a default materiality threshold around 10% or $50K.
- Project payback: month-by-month cash flow, ramp-up, break-even, project-level IRR/NPV, and a Go/No-Go signal.
- Monte Carlo simulation: probability distributions for NPV, IRR, and payback period, including percentiles and a Go/No-Go decision signal.

Boundaries

This is best treated as a financial modeling aid and investment-committee prep tool. Inputs should be JSON financial data; most scripts rely on the Python standard library, while Excel export requires openpyxl. If reliable drivers, historical cash flows, or comparable data are missing, the output should be used as a first pass, not a final investment decision.