This article investigates four models for minimizing the tracking error bet
ween the returns of a portfolio and a benchmark. Due to linear performance
fees of fund managers, we can argue that linear deviations give a more accu
rate description of the investors' risk attitude than squared deviations. A
ll models have in common that absolute deviations are minimized instead of
squared deviations as is the case for traditional optimization models. Line
ar programs are formulated to derive explicit solutions. The models are app
lied to a portfolio containing six national stock market indexes (USA, Japa
n, UK, Germany, France, Switzerland) and the tracking error with respect to
the MSCI (Morgan Stanley Capital International Index) world stock market i
ndex is minimized. The results are compared to those of a quadratic trackin
g error optimization technique. The portfolio weights of the optimized port
folio and its risk/return properties are different across the models which
implies that optimization models should be targeted to the specific investm
ent objective. Finally, it is shown that linear tracking error optimization
is equivalent to expected utility maximization and lower partial moment mi
nimization. (C) 1999 Elsevier Science B.V. All rights reserved. JEL classif
ication: C63; G11.