The Due Diligence Checklist: Essential Questions Before Trusting a Bayesian Model for Private Asset Valuation
What Key Assumptions Underlie This Valuation and How Were Priors Chosen?
Which assumptions influence the result?
Every private asset analysis is built on a foundation of assumptions. Analysts should demand to see which priors were chosen, what evidence informed them, and how sensitive the results are to those choices. Changing a single input can sometimes shift the entire valuation, so exposing these dependencies is critical for robust decision-making.
Are All Data Sources and Omissions Documented and Justified?
What sources were used or omitted?
Transparency starts with a list of data sources: what was included, what was left out, and the reasoning behind these decisions. If an asset's data is sparse or relies heavily on a single source, highlight these weaknesses openly. Analysts should document every source to allow independent review and debate.
What Sensitivity Checks Have Been Performed and Which Variables Matter Most?
How fragile are results to changes?
A robust analysis reveals how results shift if core assumptions or input values are adjusted. Sensitivity checks uncover which variables have the greatest impact on the outcome. These insights help teams decide where to focus further research and how much confidence to place in the final numbers.
Is the Model Fully Transparent and Repeatable by an Independent Analyst?
Can this analysis be independently repeated?
Models gain credibility when another practitioner can replicate the results using the same methods and data. Open documentation of the model structure, along with versioned code and notes on every step, allows for transparent peer review. Without this, stakeholders may be forced to rely on black-box estimates.
