Embracing Uncertainty: The Power of Generative Models in Financial Forecasting

In financial forecasting and performance management, conventional wisdom holds that accurate, high-quality data is the foundation of trustworthy predictions. However, this pursuit of perfect data comes at a significant cost, both in resources and missed opportunities. What if there's a better approach?

Traditional financial forecasting models often suffer from two fundamental limitations: 

• The illusion of perfect data: Despite our best efforts, collected data will always contain errors, biases, and inconsistencies.
• Historical blindness: Past data may lack relevance for future conditions, especially in rapidly changing environments.

When we obsess over obtaining "perfect" historical data, we not only invest excessive resources but also often overlook the inherent limitations of backward-looking analysis.

Generative models built on Bayesian principles offer a radically different approach that addresses these shortcomings:

Unlike classical prediction models that aim for point estimates, Bayesian generative models assume that any statement about the future is inherently uncertain. Instead of fighting this reality, they incorporate it directly into the modeling and decision process.

These models use probability distributions to quantify our beliefs about relevant questions, acknowledging that multiple realizations are possible with varying degrees of beliefs. This approach provides a more honest representation of what we can reasonably know. Generative models excel at addressing practical business questions like:

"Will our EBITDA exceed target threshold by year-end, given the revenue and expense data from the last four month?"

One of the most powerful features of the Bayesian approach is its flexibility in updating beliefs as new information becomes available. Rather than being locked into historical patterns, these models adapt quickly to emerging trends. This means present performance influences future predictions more heavily than historical data from previous years. In volatile markets or during significant transitions, this adaptive capability proves invaluable.

Transitioning to a generative modeling approach for financial forecasting and performance management involves several key steps:

1. Start with Driver-Based Models Informed by Expertise

Begin with elementary driver-based models that incorporate SME expertise rather than relying solely on historical data. These models capture the causal relationships between business drivers and outcomes.

Expert opinions serve as informed priors in the Bayesian framework, providing a starting point that already incorporates domain knowledge and business understanding.

2. Integrate These Drivers into Your 3-Statement Model

Incorporate these driver-based models into your standard financial statements (income statement, balance sheet, and cash flow statement). This integration ensures that your forecasting approach remains connected to standard financial reporting.

3. Combine Assumptions with Measured Data Using Bayes' Rule

As new data becomes available, use Bayes' rule to update your prior beliefs. This mathematical approach systematically combines your initial assumptions with observed reality to produce more refined posterior distributions. This step transforms static forecasts into dynamic predictions that continuously improve with each new data point.

4. Refine Assumptions with Each New Observation

Each time new data is observed, use it to refine your model assumptions. This continuous learning process ensures that your forecasts become increasingly well-calibrated over time (see example in Microsoft Excel). Unlike traditional models that might only be updated quarterly or annually, Bayesian models can incorporate new information as soon as it becomes available.

5. Maintain an Updated 3-Statement Model

The result is an always-current financial model that properly guides the performance management process. This dynamic approach provides leadership with a more accurate understanding of where the business stands and where it's likely headed.

The real power of generative models in finance isn't just about producing more accurate point predictions. Their value lies in providing a framework that:

• Acknowledges and quantifies uncertainty
• Adapts quickly to changing conditions
• Integrates expert knowledge with observed data
• Answers specific, decision-relevant questions
• Provides a continuous learning mechanism

Info: MCPlan is our best choice for a 3-Statement Model based on Microsoft Excel that incorporates uncertainty.