Formulating Optimal R&D Portfolios
| Autor: | Samuel B. Graves, Jeffrey L. Ringuest, Randolph H. Case |
|---|---|
| Rok vydání: | 2000 |
| Předmět: |
Operations research
Computer science Strategy and Management Decision theory media_common.quotation_subject 05 social sciences General Engineering Efficient frontier Simple (abstract algebra) Management of Technology and Innovation 0502 economics and business Portfolio 050211 marketing Operations management Project portfolio management Portfolio optimization Explicit knowledge Function (engineering) 050203 business & management media_common |
| Zdroj: | Research-Technology Management. 43:47-51 |
| ISSN: | 1930-0166 0895-6308 |
| DOI: | 10.1080/08956308.2000.11671357 |
| Popis: | Here's a simple, yet rigorous, method for generating portfolios that minimize risk for a given level of return. OVERVIEW: RD others have strong theoretical grounding but are difficult to implement. Some may rely upon unreasonably strong assumptions about the decision makers and their attitudes toward risk; others require implausibly detailed inputs describing the parameters of the decision maker's utility function. Other models exist which do not have these problems but they fail to generate truly optimal portfolios. We have developed an RD that is, portfolios that minimize risk for a given level of return. The model is grounded soundly in the same decision theory as our previous paper which, in turn, was based on concepts taken from financial portfolio optimization as originally described by Shalit and Yitzhaki (5). The model requires no explicit knowledge of the decision maker's utility function. In fact, it is applicable to all risk-averse decision makers, regardless of their utility function. The model, which we describe below, is simple and easy to implement, using only a standard EXCEL spreadsheet. It addresses directly one of the main goals of portfolio management as described by Cooper, Edgett and Kleinschmidt: portfolio balance (2). It achieves this balance through a rigorous evaluation of the risk and reward of competing portfolios. Two Applications We shall demonstrate two applications of our methodology. First, we apply it to a simple, artificial list of five hypothetical RD that is, portfolios having the lowest possible risk level for any given level of return. Then we will show how to select the preferred portfolio from that list. Our methodology requires only a minimum of data describing the projects. We simply need an estimate of the success probability for each project and the financial return if the project is successful or unsuccessful. … |
| Databáze: | OpenAIRE |
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