Intellectual Property Valuation: A Finance Perspective

By Samuel, Dorit | Albany Law Review, Fall 2007 | Go to article overview

Intellectual Property Valuation: A Finance Perspective


Samuel, Dorit, Albany Law Review


I. INTRODUCTION: FINANCE, FINANCIAL MODELING, AND THE LAW

The close relationship between law and economics has been recognized for more than four decades. Starting with the work of the British economist and 1991 Nobel prize winner Ronald Harry Coase, in his article The Problem of Social Cost, (1) and current Judge Guido Calabresi of the United States Court of Appeals for the Second Circuit, in Some Thoughts on Risk Distribution and the Law of Torts, (2) this relationship became formalized into the field of Law and Economics. (3) Today, there are centers of Law and Economics (4) throughout the legal academy both in the United States and throughout the world, and at least ten journals are dedicated to the subject. Now, with Law and Economics as a mature discipline, accepted as a regular part of the law school curriculum, attention is focusing on the relationship between law and the related--but different--field of Finance, and how constructs from that field have important relevance to the law.

Finance, originally a subfield of Economics, has grown exponentially, and, since the 1970s, has become increasingly sophisticated so that it has become differentiated from economics in general, and, academically, has moved from the Economics departments of universities to play a significant role as a distinct, independent field of study in business schools. Today, Finance, with its foundation in mathematical and statistical modeling, has taken on special prominence, particularly with respect to issues of asset valuation.

The field of Finance as a whole is not easily defined. A careful analysis, however, will lead to the realization that almost all significant activity in the field is driven by one encompassing goal: to find an efficient, accurate, and palatable way to evaluate what an asset is worth at a given time, a task that requires one to grapple with serious limitations and to operate within an array of questionable assumptions. Finance professionals have long struggled with the problem of evaluating an asset in a way that accounts for the uncertainty arising from the risk inherent in the asset's performance, the overall market conditions, and their performance interrelationship within the parameters of an assumed or assumable time-period. The core of the analytic task--and the reason the field of Finance has become so mathematically sophisticated--lies in the development and application of financial models of general applicability. Two such financial models, the Capital Asset Pricing Model (CAPM) (5) and the Black Scholes Option Pricing Model (BSOPM), (6) were the basis for Nobel prizes awarded to their creators (7), and are now the most well-known and established asset valuation models, at least in part because of their extreme efficiency of use notwithstanding some theoretical limitations. Innovations such as these in the development of asset valuation models, that at the time were a dramatic departure from accepted valuation principles, had an enormous impact on the development of the field of Finance. These constructs, whether causatively or correlatively, coincided with the recognition of Finance as a field independent of Economics and of particular importance to business and business schools, and the prominence of Finance in the business curriculum over the last quarter of a century.

Prior to the prevalence of these more sophisticated constructs, finance researchers were grappling with different approaches to the problem of evaluating an asset in a way that accounted for its various inherit risks, and quantified it originally as the standard variation of assets' returns, also known as volatility. Originally, the main concept and the initial basic building block of asset valuation was the concept of Present Value; central to this analysis is the realization that the asset value changes over time, and that change is driven by different factors. What earlier models failed to do is to evaluate how that particular change is correlated to the asset's risk and the market's risk in which this asset is traded; and integrate into their analysis the quantified risk ("volatility") in a way more comprehensive and realistic than simply consideration of statistical standard deviations. …

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