Analyzing Acquisitions in the Video Game Industry through a Real Options Lense

Abstract

This thesis represents an analysis of corporate acquisition in the video game industry through a real options lense. While the number of acquisitions in the industry is rising, merger and acquisitions in the video game industry are poorly covered by scientific literature. Thence, the aim of this thesis is to develop an algorithm for a pre-acquisition target company valuation for the video game industry. Theories clarifying motivation of acquisition were examined for setting possible scenarios of acquisition. In order to elect the most optimal and accurate valuation method for the valuation tool two classifications of target company valuation methods were observed. As the result, the fuzzy pay-off distribution real option valuation model was selected to be used as it is able to treat uncertainty related to acquisition target screening. To develop an algorithm that meets the video game industry features and dynamic nature, an overview of the video game development, industry trends, and significant cases of merger and acquisition were discussed. To develop in-depth understanding of video game industry several experts were interviewed with a semi-structured technique. Based on the interview results possible acquisition motives and potential real options were defined, and the valuation tool was designed. The valuation of target company was recounted separately for target company as stand- alone and related to the target acquisition possible synergies. Also the impact of corporate culture was included in the valuation procedure. The use of the fuzzy pay-off method, multiple one-period discount rate, and dynamic inputs allowed to accurately valuate the target company. Finally, a numerical illustration for the described algorithm was presented to demonstrate its capacities and limitations.