Improving value assessment of high-risk, high-reward biotechnology research: the role of 'thick tails'.

This paper presents work toward improving the efficacy of financial models that describe the unique nature of biotechnology firms. We show that using a 'thick tailed' power law distribution to describe the behavior of the value of biotechnology R&D used in a Real Options Pricing model is significantly more accurate than the traditionally used Gaussian approach. A study of 287 North-American biotechnology firms gives insights into common problems faced by investors, managers and other stakeholders when using traditional techniques to calculate the commercial value of R&D. This is important because specific quantitative tools to assess the value of high-risk, high-reward R&D do not currently exist. This often leads to an undervaluation of biotechnology R&D and R&D intensive biotechnology firms. For example, the widely used Net Present Value (NPV) method assumes a fixed risk ignoring management flexibility and the changing environment. However, Real Options Pricing models assume that commercial returns from R&D investments are described by a normal random walk. A normal random walk model eliminates the possibility of drastic changes to the marketplace resulting from the introduction of revolutionary products and/or services. It is possible to better understand and manage biotechnology research projects and portfolios using a model that more accurately considers large non-Gaussian price fluctuations with thick tails, which recognize the unusually large risks and opportunities associated with Biotechnology R&D. Our empirical data show that opportunity overcompensates for the downside risk making biotechnology R&D statistically more valuable than other Gaussian options investments, which may otherwise appear to offer a similar combination of risk and return.