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
Music and snow. Poke my eyes out Perhaps your immediate response is: ”I’d rather poke my eyes out with a burning stick than do data analysis.” There’s a completely different [...]
jun 25, 26
Move your data analysis to a computing environment specifically designed for it. Why R and not spreadsheets? Here are three reasons: complexity graphics money Spreadsheets are easily overwhelmed. Very complex [...]
jun 25, 26
The steps taken to fix an R problem. Task To prepare for the Portfolio Probe blog post called ”Implied alpha and minimum variance”, I tried to update a matrix of [...]