Beyond Discounted Cash Flows: An Actuarial Approach to Business Valuation under Intangible Asset Uncertainty
6 viewsAbstract
The modern economy has shifted from tangible, asset-heavy industrial models to knowledge- based sectors where value is primarily driven by intangible assets, such as artificial intelligence, proprietary algorithms, and customer ecosystems. Traditional valuation techniques, including the deterministic Discounted Cash Flow (DCF) model, frequently fail to capture the profound uncertainty inherent in these assets, often leading to the mispricing of technology firms. This challenge is particularly acute in emerging economies like Lebanon, where rapid digital growth occurs amidst significant macroeconomic volatility. This paper addresses this limitation by developing an integrated valuation framework that complements traditional corporate finance with actuarial science. Utilizing a mixed-methods approach, the study performs a comprehensive literature review and analyzes five case studies, including the Lebanese platform Toters, to identify the shortcomings of conventional models. The quantitative component extends the DCF approach through stochastic modeling, using 10,000 Monte Carlo simulations to treat key valuation drivers—such as innovation success and customer retention—as random variables. The results demonstrate that the actuarial framework provides a superior representation of enterprise value by generating a probability distribution rather than a single point estimate. By employing advanced risk metrics like Value at Risk (VaR) and Conditional Value at Risk (CVaR), the model explicitly quantifies downside risk and potential upside volatility. This research concludes that incorporating actuarial mathematics transforms business valuation into a robust, probabilistic assessment, offering investors and corporate managers a more realistic and comprehensive tool for navigating the complexities of the modern digital economy.
Keywords
Actuarial Science, Business Valuation, Intangible Assets, Monte Carlo Simulation, Stochastic Modeling