7. Conclusions and discussion
The literature review shows that methods of portfolio formation based on intangibles are limited by the analysis of a single component of intangibles, usually human capital, R & D expenses, or advertising costs In addition, prior studies usually only take into account intangibles’ quantity (wage per employee, R & D, and advertising expenses, etc.). We argue that a more essential feature of intangibles is their quality, in other words, their ability to create value. Therefore, we propose a method that distinguishes two attributes of intangibles based on their uniqueness: generic and unique resources. Generic intangible resources are widely spread across the market whereas unique resources are much less common. We distinguish these two attributes by comparing a company's portfolio of intangibles and a market portfolio based on their efficiencies. According to Stewart (1997), Sveiby (1997), and Nold (2012), companies that use intangibles efficiently raise their M/B ratio and, therefore, are attractive to investors. Our proposed EUGIn method predicts which companies have value growth potential on the basis of their ability to use unique intangibles efficiently. To use our model to make investment decisions, we must explicitly control for a potential data-snooping bias: “There is always the possibility that any satisfactory results obtained may simply be due to chance rather than to any merit inherent in the method yielding the results” (White, 2000, p. 1115). We examine the statistical significance of the performance and performance persistence of the best and worst performing companies with a flexible bootstrap procedure applied to the EUGIn. We apply the proposed algorithm to a data set that consists of S & P500 companies and covers the period from 2000 to 2012.
