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Adaptive Valuation Approaches in Life Sciences and Healthcare

In finance, theories about the efficiency of markets have been both extraordinarily elegant and disastrously wrong on many occasions. These so-called efficient markets concepts have brought about interesting tools like the Capital Asset Pricing Model and Black-Scholes or Cox-Ross-Rubinstein binomial lattice models as tools for understanding how to assess a company’s cost of capital or how to value various derivative equity instruments.

Unfortunately, how we are applying these valuation and modeling techniques to early stage life sciences and healthcare company valuation processes is creating problems that need to be addressed (which I will attempt to address over a series of articles and posts). But first, some background.

Andrew Lo, in his new book Adaptive Markets, outlines the notion that our financial models have been based on physics-oriented ideas whereas the realities of capital markets follow something that looks closer to biology-based models. It is not that the efficiency of markets is inaccurate, rather it is simply a theory that is incomplete. Efficient markets are what occur when the same game in the same environment gets played over and over such that, over time, the players operate with brilliant efficiency. However, when exogenous shocks occur to the system, market efficiencies fade away as participants must create new heuristics for how the new environment will work. The adaptive heuristics that show merit survive and thrive whereas the others fade away. If there are no other real shocks to the system, markets move back to efficiency as the players with the most adaptive investment theses prevail and compete against each other…until the next shock.

The ability to have numerous cycles of trial and error to test ways of thinking, living, investing, selling, meditating, designing, etc. is critical to the success that can be understood by finding the most adaptive traits to the current environment which breed success. Any biologist can outline the ways in which random mutation (and these days “not so random” mutation) can affect an organism’s ability to thrive in different environments. In the financial world, Lo calls this “evolution at the speed of thought” as it is in the heuristics of our thoughts about investment theses and practices (typically enacted by hedge funds) that we see these ideas take hold.

In the world of Silicon Valley, Boston, and other innovation-economies, this same adaptive process and these same heuristic-oriented ideas around innovative companies and products are the breeding grounds for extraordinary success and for catastrophic failure (I’ve observed many instances of both in my career working with biotechnology and life science companies). Companies at the “four dudes in a garage” stage have many, many systemic issues that must be addressed to create a product that will sell in the real world. These issues are not simply product-oriented, but also represent the very human issues of recruiting, organizing and motivating people, often with the most serious issue of simply getting everyone rowing in the same direction. In the biotechnology world, there are other significant concerns like developing interest from partners in behemoth organizations and working successfully (and collaboratively) with said partners to further research and development. Many a company has stagnated (or worse) because the adaptive heuristics around product development, sales, BD, external alliances and internal organization dynamics have failed in their development. In biologic terms relative to the failures, the adaptive heuristics pursued were not aligned with the environment and not enough resources were available to allow for further adaptations.

To be clear, companies and products are significantly de-risked when the heuristics for development prove to be successful in the environment. When we see companies going public and continuing to succeed, the traits that were selected as the company evolved from “four dudes in a garage” to its more successful stage were ultimately internalized and systematized through trial and error. As a result, the magnitude and type of exogenous shock that has an impact on the company will change. The company moves from a “key person gets hit by the proverbial bus”-type of risk to shocks that become industry or economy specific (the issues that become big shocks for a small venture-backed company are different than those at a Microsoft or a Gilead). In essence, these successful companies become more and more like the efficient markets that physics-envious economists have drooled over (their successful ideas won over time to make them successful). But, at the early stages, the efficient “success” models in no way, shape or form represent an accurate portrayal of the state of the early stage, angel and venture capital-backed companies. Small teams of 10 have not likely implemented the adaptive traits that will allow for their success at larger and larger scales (the rule of “3 and 10” basically says an organization has to change everything as a company scales from 3 to 10 to 30 to 100 to 300 and so on). Having more capital or promises of more capital allows for the ability to have more lifecycles on success and failures, which is good, but there are still numerous human and non-human risks that must be solved for.

This makes the valuation techniques that many have been using for the valuation of early stage, venture capital-backed companies (typically for option expense granting purposes or for IRS 409a compliance-based purposes) interesting but incomplete.

Over the course of the next few weeks, I will be publishing additional articles that speak to the challenges of using the finance tools of efficient markets and applying them to early stage companies (from “dudes in the garage” or “scientists in someone else’s lab” stages on through to later, more successful stages). These articles will cover:

  • Why the perspective on value matters and whether fair value is the same as fair market value;
  • Adaptive heuristics for the entrepreneur or entrepreneurial employee – does the decision framework for the entrepreneur or the employee change if we stay on our current path?;
  • What should we do going forward? Should our methodologies be adaptive as well? If so, what might some of those changes be?

To be clear, we’ve come a long way in terms of the tools and techniques that can be used for valuing companies with complex capital structures and many of these approaches that have been implemented as best practices are better than what they were when we started this process over a decade ago. In my opinion, the models hold reasonably well if one is to take an investor’s perspective (acting as an agent of a fund with 15 shots on goal with someone else’s money). But they break down when one looks at the common securities from the perspective of those that actually hold said securities (the employees and entrepreneurs). Further, it’s hard to argue a hypothetical buyer / seller transaction when one excludes the perspectives of those that actually hold the securities. This creates a gap and one in which I’ll explain in a later post.


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