Bayesian Learning and Real Options: Valuing Strategic Investments
A Bayesian Learning Real Options model integrating Bayesian decision-making with real options pricing, applied to valuing a parts manufacturing license in aerospace.
This paper presents a Bayesian Learning Real Options (BLRO) model that integrates Bayesian decision-making with real options pricing. By actively acquiring new information, firms can update investment decisions over time, rather than passively waiting for uncertainty to resolve.
Model Overview
The BLRO framework treats investment thresholds as dynamic objects that shift as firms learn. Unlike classical real options models that assume parameters are known, the Bayesian approach formally incorporates estimation uncertainty and updates it as data arrives.
Application: Aerospace License Valuation
The model is applied to valuing a parts manufacturing license in the aerospace industry. The application illustrates how learning sharpens decision thresholds and better aligns strategic investments with real-world uncertainty.
Key Finding
The BLRO framework helps firms prioritize long-term strategic growth despite uncertain conditions — and demonstrates that the value of information is quantifiable and material in capital allocation decisions.