  We consider the problem of selecting a portfolio of assets that provides the
investor a suitable balance of expected return and risk. With respect to the
seminal mean-variance model of Markowitz, we consider additional constraints on
the cardinality of the portfolio and on the quantity of individual shares. Such
constraints better capture the real-world trading system, but make the problem
more difficult to be solved with exact methods. We explore the use of local
search techniques, mainly tabu search, for the portfolio selection problem. We
compare and combine previous work on portfolio selection that makes use of the
local search approach and we propose new algorithms that combine different
neighborhood relations. In addition, we show how the use of randomization and
of a simple form of adaptiveness simplifies the setting of a large number of
critical parameters. Finally, we show how our techniques perform on public
benchmarks.
