Portfolio Analysis with Partial Information: The Case of Grouped Data
研究投资者仅知道股票分组及组内平均特征时，如何利用投资组合理论做出最优决策，对依赖有限信息进行资产配置的实践者具有参考价值。
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.