Optimising Investment Decisions in R&D Intensive Private Micro-Entities Using Game Theory

In this article, three small private R&D intensive European entities have been used in a case study involving game theory combined with content analysis in an attempt to identify an optimal investment strategy. A game theory matrix is constructed for each entity based on previous exposure of investors to the entities’ capital sources. The basic concept is that the investment exposure’s size is affected by the capitalisation of internally generated intangible assets; in other words, investors consider capitalisation of intangible assets as a positive signal regarding the future economic benefits associated with the intangible asset, and as a result, they adjust their investment positions accordingly. The matrices aim to identify an optimal investment strategy in high-intensity R&D private micro-entities. The game theory matrices are constructed using publicly available empirical data extracted from the financial statements of three R&D intensive private micro-entities. The game theory matrix attempts to estimate the effect of the managerial discretionary choice to capitalise or expense the development cost of internally generated intangible assets; the risk appetite of investors could be affected by the capitalisation signalling. The investment strategies are classified based on their risk in three categories. High risk is represented by equity; medium risk is represented by long-term debt, and low risk is represented by short-term debt. The results of the game theory matrices indicate that if a potential investor were to select an investment strategy after the end of the investigated time frame, end of 2015 for one entity and 2016 for the other two, the dominant strategy would be a medium risk through long-term debt for one company and low risk for the other two. These dominant strategies are then evaluated ex-post by reviewing the financial positions of the entities according to the most recent financial statements and additional relevant documentation.