Developing a New, Probabilistic Model of Asset Pricing

23 Pages Posted: 6 May 2009 Last revised: 21 Aug 2009

Date Written: April 28, 2009

Abstract

The “market” is too complex to model deterministically, and as a result it is impossible to deterministically and accurately price assets within the market. Nevertheless, the vast majority of financial asset pricing models, such as the Capital Asset Pricing Model (CAPM), are employed as deterministic with assumptions of normally, independently, and continuously distributed price fluctuations. The inability of such assumptions to accurately reflect real market conditions has been thoroughly documented, most notably by Benoit Mandelbrot.

With this in mind, this work proposes an alternative, probabilistic asset pricing model based in utility theory to determine the likelihood that an asset will generate a return in excess of the risk-free rate. The relationship of this likelihood to the risk-free rate is used to calculate a discount rate for the asset. The factors which most significantly affect a particular asset’s likelihood of out-performing the risk-free rate are then inferred through logistic regression and further researched through processes such as the Toyota Five-Whys line of questioning for the purpose of better managing the odds of beating the risk-free rate.

Implied in such a probabilistic, risk-management model is that there need be no difference in the valuation of for-profit and not-for-profit organizations. Furthermore, this method suggests that practitioners of finance move away from seeking to enhance the value of tomorrow’s dollar with inaccurate, deterministic models and instead ensure the value of today’s dollar by better managing an organization’s odds of beating the risk-free rate.

Keywords: Finance, Asset Pricing, Probabilistic, Valuation, Logistic Regression, CAPM, Deterministic, Inference

JEL Classification: B21, C44, C78, D46, D81, G12

Suggested Citation

Charlton, Jonathan Ross, Developing a New, Probabilistic Model of Asset Pricing (April 28, 2009). Available at SSRN: https://ssrn.com/abstract=1396503 or http://dx.doi.org/10.2139/ssrn.1396503

Jonathan Ross Charlton (Contact Author)

Charlton Analysis Group, LLC ( email )

4101 Navajo Trail
Atlanta, GA 30319
United States
404-410-8998 (Phone)
866-579-6414 (Fax)

HOME PAGE: http://www.charltonag.com

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