Portfolio Analysis with Partial Information: The Case of Grouped Data

Almost all of the literature in finance analyzing the selection of optimum portfolios assumes that the agent making the decision has a full set of estimates of the expected return for each security and the variance covariance matrix between securities. In actual practice most decision makers simply receive a list of stocks with ranking on each stock and perhaps some partial risk information. The purpose of this paper is to determine what we can learn from portfolio theory about optimum decisions if all the investor knows is the grouping of stocks plus at best the average characteristics of the stocks in a group. This analysis is important because these are the data most investors utilize to make their portfolio decisions.

• 저널Management Science · 33(10) · 1238–1246
• 토픽Financial Markets and Investment Strategies · Finance
• DOI10.1287/mnsc.33.10.1238
• 저자Edwin J. Elton, Martin J. Gruber