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
I’d like to do a song of great social and political import. The code that created the illustrations in Tao Te Programming is now available as the TaoTeProgramming package on [...]
jun 25, 26
Another of the all ye entering here. Issue When subscripting an xts object, columns that don’t exist in the object are silently ignored. Example First, create an xts object: xtx [...]
jun 25, 26
Posts by page views Interview with a forced convert to R from Matlab A first step towards R from spreadsheets Plot ranges of data in R A statistical review of [...]