Supply Chain Financing and Option Theory : Defiling the Optimal Discount Rate

Abstract

Supply chain financing (SCF) has gained significant popularity during the past decade, but the research conducted on the topic mostly focuses on a few very specific solutions. Dynamic discounting, a supply chain financing solution that utilizes an IT-platform between the buyer and supplier firms in order to dynamically grant discounts on the invoiced amounts, is one of the newest and most advanced SCF solutions. However, the research, both theoretical and empirical, is significantly lacking behind on the topic. This master’s thesis offers a novel theoretical approach to estimating a range of suggested discount rates to be applied in a dynamic discounting program. The minimum rates accepted by the buyer company are estimated by applying the Black-Scholes option theory framework, therefore assuming the suppliers’ accounts receivables to be contingent claims on the total equity of the buyer company. The maximum rates are assumed to equal to the supplier’s mean weighted average cost of capital, therefore forming a theoretical range in which the final chosen discount rate should lie within. Finally, PLS regression is applied as a methodology to the calculated minimum discount rates in order to further study the impact and predictive abilities of certain corporate size and performance measures, yielding into the finding that these measures also serve as great predictors of the estimated discount rates.