4 min read Investment Learning

How to Set Distributions for Revenue Growth Monte Carlo

How to Set Distributions for Revenue Growth Monte Carlo
Photo by Austin Distel / Unsplash

Understanding future revenue growth is critical for any serious investor. Relying on a single point estimate for growth introduces significant risk. This guide explains how to set distributions for revenue growth Monte Carlo simulations, providing a more robust approach to valuation. We aim for clarity, precision, and a focus on fundamental principles.

Why Use Monte Carlo for Revenue Growth?

The future is uncertain. Revenue growth is not a fixed number; it is a range of possibilities. A single forecast can be misleading. A Monte Carlo simulation addresses this by modelling a range of potential outcomes. It helps us understand the probability of different growth scenarios, leading to better investment decisions.

Understanding Revenue Growth Drivers

Before setting distributions, understand what drives a company's revenue. Is it volume, price, new products, market expansion, or market share gains? Each driver might have its own level of uncertainty and historical pattern. A deep understanding of the business is paramount.

Step-by-Step: Setting Revenue Growth Distributions

1. Gather Historical Revenue Growth Data

Your starting point is historical performance. Accessing reliable, granular data is essential. Use tools like Screenwich to quickly find a company's past revenue figures. Look at annual and quarterly growth rates over a meaningful period, typically 5-10 years. Screenwich provides comprehensive financial statements, making this step straightforward.

2. Analyse Historical Data

Once you have the data, analyse it. Plot the historical growth rates. Look for:

  • The average growth rate.
  • The volatility or standard deviation of growth.
  • Any discernible patterns, trends, or cycles.
  • Outliers or one-off events that skewed past results.

This analysis informs your choice of probability distribution.

3. Select Appropriate Probability Distributions

This is where the art meets the science. The distribution you choose should reflect the underlying business dynamics and historical data. Common distributions include:

  • Normal Distribution: Symmetrical, bell-shaped. Suitable for mature, stable businesses with growth rates that tend to cluster around an average. Use when growth can be positive or negative, and extremes are less likely.
  • Uniform Distribution: Every value within a defined range is equally likely. Use when you have a clear minimum and maximum growth rate, but no strong central tendency. Perhaps for a new product with highly uncertain initial uptake.
  • Triangular Distribution: Defined by a minimum, maximum, and a most likely value. Useful when you have an educated guess for the peak growth rate, alongside a plausible range.
  • Lognormal Distribution: Skewed to the right, with a long tail of higher values. Often appropriate for growth rates that cannot be negative and have a higher probability of moderate growth, but also a chance of exceptional growth. This is common for early-stage or high-growth companies.

Consider the business's stage, industry, and competitive landscape. A high-growth tech firm might use a Lognormal distribution, while a utility company might use a Normal or Triangular distribution.

4. Define Distribution Parameters

Once a distribution is chosen, you need to set its parameters:

  • For Normal/Lognormal: Use the historical mean and standard deviation from your data analysis. Adjust these based on forward-looking insights.
  • For Uniform: Define the minimum and maximum growth rates.
  • For Triangular: Define the minimum, maximum, and most likely growth rates.

These parameters should not solely rely on history. Incorporate management guidance, industry forecasts, and your own qualitative assessment. For instance, if a company has just entered a new market, its future growth might be higher than its historical average. Keep an eye on the earnings calendar for upcoming announcements that might shift these expectations.

5. Integrate into Your Valuation Model

With your revenue growth distributions defined, you can integrate them into your Discounted Cash Flow (DCF) model. A `DCF calculator` will then use these distributions within a `Monte Carlo simulation` to generate a range of possible future cash flows. This process helps you determine a range of `intrinsic value` outcomes, rather than a single point estimate. Remember to also consider other key inputs like `WACC` and `terminal value` in your `stock analysis`.

Common Mistakes to Avoid

  • Using a Single Point Estimate: The biggest error is ignoring uncertainty altogether.
  • Blindly Applying Normal Distribution: Not all data is normal. Misrepresenting the underlying process leads to flawed results.
  • Ignoring Qualitative Factors: Market shifts, new technologies, and competitive threats can drastically alter growth trajectories. Historical data alone is insufficient.
  • Using Insufficient Data: Too few historical data points lead to unreliable parameter estimates.
  • Over-Complicating: Start simple. A complex model with poor assumptions is worse than a simple, well-reasoned one.
  • Not Updating Assumptions: Business environments change. Regularly revisit and update your growth assumptions and distributions.

Checklist for Setting Revenue Growth Distributions

  1. Understand the Business: What drives its revenue?
  2. Gather Data: Collect robust historical revenue growth data from reliable sources like Screenwich.
  3. Analyse Data: Plot and statistically examine historical growth rates.
  4. Select Distribution: Choose the probability distribution that best reflects the business's nature and historical patterns.
  5. Define Parameters: Set the min, max, mean, standard deviation, or most likely values, blending historical data with forward-looking insights.
  6. Integrate and Simulate: Incorporate these distributions into your DCF model for a `Monte Carlo simulation`.
  7. Review and Refine: Continuously challenge and update your assumptions.

By following these steps, you can build a more realistic and robust valuation model, better accounting for the inherent uncertainties in forecasting revenue growth.

Screenwich