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OPTIMAL EXIT FROM A DETERIORATING PROJECT WITH NOISY RETURNS

Published online by Cambridge University Press:  22 June 2005

Reade Ryan  and
Steven A. Lippman
Reade Ryan
Affiliation:
The John E. Anderson School of Management at UCLA, Los Angeles, CA 90095-1481, E-mail: rryan@amaranthllc.com
Steven A. Lippman
Affiliation:
The John E. Anderson School of Management at UCLA, Los Angeles, CA 90095-1481, E-mail: slippman@anderson.ucla.edu
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Abstract

We consider the problem of determining when to exit an investment whose cumulative return follows a Brownian motion with drift μ and volatility σ2. After an unobserved exponential amount of time, the drift drops from μH > 0 to μL < 0. Using results from stochastic differential equations, we are able to show that it is optimal to exit the first time the posterior probability of being in the low state falls to p*, where the value of p* is given implicitly. We effect a complete comparative statics analysis; one surprising result is that a decrease in μL is beneficial when |μL| is large.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 19 , Issue 3 , July 2005 , pp. 327 - 343
Copyright
© 2005 Cambridge University Press

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References

REFERENCES

Abramowitz, M. & Stegun, I.A. (1972). Handbook of mathematical functions. Washington DC: US Government Printing Office.
Deuschel, J.D. & Stroock, D.W. (1989). Large deviations. Boston: Academic Press.
Jensen, M. (1993). The modern industrial revolution, exit, and the failure of internal control systems. Journal of Finance 48: 831–880.Google Scholar
Mansfield, E. (1968). The economics of technological change. New York: W.W. Norton.
Massey, C. & Wu, G. (2001). Detecting regime shifts: The causes of over- and underreaction. Working paper, Graduate School of Business, University of Chicago, Chicago, IL; to appear in Management Science.
Miles, F.C. (1949). Motive power ordered in 1948. Railway Age 212.
Oksendal, B. (1998). Stochastic differential equations: An introduction with applications, 5th ed. Berlin: Springer-Verlag.
Postrel, S.R. (1990). Competing networks and proprietary standards: The case of quadraphonic sound. Journal of Industrial Economics 39: 169–185.Google Scholar
Rosenberg, N. (1972). Factors affecting the diffusion of technology. Explorations in Economic History 9: 3–33.Google Scholar
Rosenberg, N. (1976). On technological expectations. The Economic Journal 86: 523–535.Google Scholar
Rosenberg, N. (1982). Inside the black box: Technology and economics. Cambridge: Cambridge University Press.
Ryan, R. & Lippman, S.A. (2003). Optimal exit from a project with noisy returns. Probability in the Engineering and Information Sciences 17: 435–458.Google Scholar
Schumpeter, J.A. (1950). Capitalism, socialism, and democracy, 3rd ed. New York: Harper & Row.
Starkey, D.J. (1993). Industrial background to the development of the steamship. In R. Gardiner (ed.), The advent of steam: The merchant steamship before 1900. Annapolis, MD: Naval Institute Press.
Staw, B.M. & Ross, J. (1987). Knowing when to pull the plug. Harvard Business Review 65(2): 68–74.Google Scholar