by Patrick Burns.
Abstract: Problems with performance measurement using information ratios relative to a benchmark are exposed. Random portfolios (that obey constraints but disregard utility) are shown to measure investment skill effectively. Investment mandates can also be based on random portfolios — this allows active fund managers more freedom to implement their ideas, and provides the investor more flexibility to gain utility. The issue of the proper attitude towards tracking error is broached, but left largely undecided. There is also a critique of Fisher’s method of combining p-values that shows Stouffer’s method to be preferable.
This version: 2004 December 02 (pdf)
A revised version of this with some additional material appears as ”Random Portfolios for Performance Measurement” in Optimisation, Econometric and Financial Analysis E. Kontoghiorghes and C. Gatu, editors. Springer.
jun 25, 26
The email address patrick@burns-stat.com was out of action for a few hours today. It is back now.
jun 25, 26
Customization in R. Basics Several features benefit from being customizable — either because of personal taste or specifics of the environment. The way R implements this flexibility is through the [...]
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How to control the limits of data values in R plots. R has multiple graphics engines. Here we will talk about the base graphics and the ggplot2 package. We’ll create [...]