Monte Carlo Company Valuation

Learning objectives

• Explain the main ai/ml decision that Monte Carlo Company Valuation is designed to support.
• Use Scenario, Current acquisition price / initial investment (₹ crore) to test how different assumptions change the scenario.
• Interpret 3. Simulation output, 4. Statistical summary in plain language and connect them to an action or conclusion.
• State one limitation, risk, or governance consideration before using the result in a real decision.

Run modes

• Supported modes: Browser
• Demo type: Interactive browser demo
• Primary launch surface: index.html

Before you start

Starts immediately in browser with no installs, no API keys, and classroom-safe defaults.

This helps set classroom expectations before students click into the live experience.

Core context

Look for the acquisition price, five-year free-cash-flow path, terminal value, WACC, and NPV distribution.

Observe how growth volatility, margin volatility, WACC, and terminal growth widen or tighten the valuation range.

Concepts covered

What students should note

Note that a high mean valuation can still be risky; students should compare mean NPV with downside probability and P10/P90.

List of steps

• Choose the run mode that fits the class: Browser.
• Review the default assumptions before changing anything.
• Change one or two inputs, then use `Reset defaults`.
• Read 3. Simulation output first, then compare any supporting metrics, charts, or AI text.
• Capture one insight, one limitation, and one action recommendation.

Input variables explained

• `Scenario` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Current acquisition price / initial investment (₹ crore)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Base-year revenue (₹ crore)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Mean annual revenue growth (%)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Revenue growth volatility (%)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Mean EBITDA margin (%)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `EBITDA margin volatility (%)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.
• `Tax rate (%)` changes one part of the scenario; increase or decrease it deliberately and watch how the output shifts.

Decision buttons explained

• `Reset defaults` returns the demo to a known starting state so students can begin a fresh comparison.
• `Run simulation` is the main action that computes, compares, or generates the next result from the current inputs.
• `Export JSON` saves the current result so learners can document evidence or compare scenarios later.
• `Export CSV` saves the current result so learners can document evidence or compare scenarios later.

Outputs explained

• `3. Simulation output` should be read as evidence for the decision, not just a display element. Ask what high, low, or changing values imply.
• `4. Statistical summary` should be read as evidence for the decision, not just a display element. Ask what high, low, or changing values imply.

What to notice

• Look for the acquisition price, five-year free-cash-flow path, terminal value, WACC, and NPV distribution
• Observe how growth volatility, margin volatility, WACC, and terminal growth widen or tighten the valuation range
• Note that a high mean valuation can still be risky; students should compare mean NPV with downside probability and P10/P90
• Compare the headline output with supporting views such as 3. Simulation output, 4. Statistical summary before drawing a conclusion

Discussion and reflection

• What business or technical decision would you make differently after using Monte Carlo Company Valuation?
• If you changed one assumption and ran `Reset defaults`, which output moved the most and why?
• What would you still want to validate with real data, policy, or expert review before acting on the result?