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2006 | nr 1 Financial markets : principles of modeling forecasting and decision-making | 129--139
Tytuł artykułu

R&D Portfolio Selection Based on Conditional Stochastic Dominance

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EN
Abstrakty
EN
The main goal of this chapter is to answer the questions how to analyze an existing research and development (R&D) portfolio in order to make decisions as to whether to add projects or remove projects from the existing set. This problem was considered recently by Graves and Ringuest (1996) who used a specific risk-averse utility function to demonstrate the necessity for considering new projects in the context of the existing portfolio. Their work shows the importance of portfolio context to evaluating R&D projects, but the need for explicit knowledge of the decision maker's utility function makes their approach impractical. Shalit and Yitzhaki (1994) however, have developed an analysis of stock portfolios which requires no knowledge of the decision-maker's utility function; they use conditional stochastic dominance to analyze the investor's decisions about an existing securities portfolio and show that a risk-averse investor should take into account the contents of an existing portfolio when deciding to add or remove a stock. In this chapter, we will apply this methodology to R&D portfolio. Shalit and Yitzhaki use historical market data to construct empirical probability distributions of returns for portfolio. Analogous empirical data are not available for R&D portfolios and it is impractical to analytically generate probability distributions of large portfolios. Therefore, we can use a simulation to construct probability distributions on R&D portfolio returns. (fragment of text)
Twórcy
  • The Karol Adamiecki University of Economics in Katowice, Poland
Bibliografia
  • Graves S. B., Ringuest J. L. (1996), "Evaluating a Portfolio of R & D Investments", High Technology Management Research, 7(1), 53-60.
  • Shalit H., Yitzhaki S. (1984), "Mean-Gini, Portfolio Theory, and the Pricing of Risky Assets", Journal Finance, 39(5), 1449-1468.
  • Shalit H., Yitzhaki S. (1994), "Marginal Conditional Stochastic Dominance", Management Science, 40(5), 670-684.
  • Trzpiot G. (1999), "Classification Stock Market Investment Projects by Multivalued Stochastic Dominance", in: Skulimowski A. (ed.), Financial Modelling, Cracow: Progress Business, 213-229.
  • Trzpiot G. (2002), "Multicriterion Analysis based on Marginal Conditional Stochastic Dominance in Financial Analysis", in: Trzaskalik T., Michnik J. (ed.), Multiple Objective and Goal Programming, series: Advances in Soft Computing, Berlin: Springer-Verlag.
  • Trzpiot G. (2004), "Preference Relations in Ranking Multivalued Alternatives in Finance Using Stochastic Dominance", Acta Universitatis Lodziensis, Folia Oeconomica, 175, 161-173.
  • Trzpiot G. (2005), "Partial Moments and Negative Moments in Ordering Asymmetric Distribution", in: Baier D., Wernecke K.-D. (eds.), Innovations in Classification, Data Science and Information Systems, Proc. 27th Annual GFKL Conference, University of Cottbus, March llth-14th, 2003, Heidelberg, Berlin: Springer-Verlag 181-188.
  • Weber R., Werners B., Zimmerman H. J. (1990), "Planning Models for Research and Development", European Journal of Operational Research, 48, 175-188.
Typ dokumentu
Bibliografia
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