This course provides a practical framework for incorporating probability analysis into your financial projections, equipping you with the tools and techniques to make more informed and robust financial forecasts. By incorporating probability analysis into your financial projections, you can better understand and manage risk, make more informed decisions, and improve the accuracy of your financial forecasts. This course equips you with the knowledge and tools to confidently navigate the complexities of uncertainty in the financial world.

This is a combination of two mini-courses that show how using probability analysis can improve financial decisions and projections.

Part 1: Decision Optimization by Maximizing Expected Value.

Expected value (EV) analysis is a powerful way to distill a massive array of possibilities into a few numbers, which helps clarify the most profitable course of action. EV uses a simple calculation based on potential outcomes and their probability. The first lesson walks you through the calculation and points out limits to it. We then look at influence diagrams, which are a good way to get a high-level view of the key factors in an expected value decision.

Next, we look at decision trees. Decision trees show the sequence of decisions and uncertainties. Every path through the tree is a scenario for which we can calculate an EV. The tree shows a range of outcomes, their probabilities, and the value of those outcomes. This helps us assess and clearly communicate both the EV and risk. We can better help evaluate the odds and magnitude of gaining – or losing – money.

Part 2: Better Forecasting by Projecting Ranges of Outcomes.

Accounting is the language of business. Statistics is the language of uncertainty and risk. Financial analysts and decision-makers must speak both accounting and statistics; otherwise, we ignore uncertainty and risk.

This is a very high-level overview of probabilistic analysis. I'll focus more on concepts than calculations. My hope is that these concepts improve your company's understanding and measurement of uncertainty so that you can make better decisions.

In most forecasts and budgets, great effort is spent on determining the "right input number" or a few "right numbers" for the projection model. The assumption is that if we get the right inputs, we'll get the right output. In stochastic analysis, the model inputs are a range of numbers with assumptions about how those numbers are distributed throughout that range. Projections that provide ranges of outcomes and probabilities of those outcomes give us a much more complete picture of an uncertain and risky future.

The first lesson explores the benefits of probabilistic analysis. The second lesson briefly covers a few basic statistics concepts. We'll then look at S-curves and flying bars. These are ways to display a range of data. Next is an overview of Monte Carlo simulation. We'll then explore the implementation process for simulation analysis.