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There is 1 module in this course
Are you ready to elevate your financial forecasting from single-point estimates to a powerful, probabilistic view of the future? This advanced course is designed for financial analysts who want to master Monte Carlo simulation to quantify risk and make more robust, data-driven decisions. You will learn to move beyond basic forecasting by first analyzing historical data to create a range of plausible, scenario-based projections.
Through a series of expert-led videos and hands-on exercises, you will learn how to identify the key, uncertain variables in your financial models—like costs and market demand—and define appropriate probability distributions for them. The core of the course is a step-by-step guide to executing a Monte Carlo simulation in Excel, allowing you to generate a full probability distribution of potential financial outcomes, such as EBITDA. You will not only run the simulation but also learn to interpret the resulting probability curve to answer the ultimate business question: "What is the likelihood we will hit our target?"
Please note: This course assumes prior completion of training in building and auditing financial models, or equivalent experience in constructing integrated 3-statement models in Excel.
This comprehensive module transforms your financial forecasting approach. You'll start by exploring why single-point forecasts are dangerously incomplete and discover the power of probabilistic analysis. You'll then learn what key uncertain variables are and how to define their behavior using probability distributions. Next, you'll watch a demonstration of how a Monte Carlo simulation is performed. You will then apply your skills by setting up simulation inputs before demonstrating your mastery by assessing the results of a pre-run simulation, enabling you to communicate financial risk and uncertainty with confidence and precision.
What's included
3 videos2 readings3 assignments
Show info about module content
3 videos•Total 24 minutes
Beyond the Average: The Need for Probabilistic Forecasting•6 minutes
Defining Uncertainty: Key Drivers and Probability Distributions•9 minutes
How It's Done: A Walkthrough of a Monte Carlo Simulation•9 minutes
2 readings•Total 40 minutes
From History to Hypothesis: Creating Scenario-Based Projections•20 minutes
Choosing the Right Distribution: A Practical Guide (Normal, Triangular, etc.)•20 minutes
3 assignments•Total 90 minutes
Analyzing a Probabilistic Forecast•30 minutes
Hands-On Learning: Setting Up the Simulation Inputs•30 minutes
Hands-On Learning: Interpreting the Curve: Making Decisions with Simulation Results•30 minutes
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