Valuing Innovation Under Uncertainty: A Real Options Approach to WeRide's R&D in Autonomous Vehicles

Abstract

Traditional valuation methods like discounted cash flow (DCF) often struggle to capture the value of research and development (R&D) projects due to their uncertainty and flexibility, particularly in innovative fields like autonomous vehicles (AV). This thesis explores whether real options valuation (ROV) offers a superior approach, using WeRide, a leading AV company, as a case study. The study applies both DCF and a drift-adjusted binomial ROV model to evaluate WeRide’s enterprise value – as a proxy for R&D investments. Contrary to expectations, ROV produced a lower enterprise value than DCF, driven by specifically added uncertainties such as technological and financing risks. However, ROV effectively highlighted the value of managerial flexibility – options to expand, contract, maintain, and salvage – which DCF ignored. Sensitivity analysis showed DCF’s reliance on internal cost factors versus ROV’s sensitivity to market dynamics. These findings suggest that while ROV requires precise calibration, it enriches valuation by addressing uncertainty and adaptability. A hybrid DCF-ROV framework is proposed for innovation-driven firms. This research advances valuation practices by applying ROV to the AV sector, emphasizing the need for flexible models in uncertain environments.