  In this paper we apply second order stochastic dominance (SSD) to the problem
of enhanced indexation with asset subset (sector) constraints. The problem we
consider is how to construct a portfolio that is designed to outperform a given
market index whilst having regard to the proportion of the portfolio invested
in distinct market sectors. In our approach, subset SSD, the portfolio
associated with each sector is treated in a SSD manner. In other words in
subset SSD we actively try to find sector portfolios that SSD dominate their
respective sector indices. However the proportion of the overall portfolio
invested in each sector is not pre-specified, rather it is decided via
optimisation. Computational results are given for our approach as applied to
the S\&P~500 over the period $29^{\text{th}}$ August 2018 to $29^{\text{th}}$
December 2023. This period, over 5 years, includes the Covid pandemic, which
had a significant effect on stock prices. Our results indicate that the scaled
version of our subset SSD approach significantly outperforms the S\&P~500 over
the period considered. Our approach also outperforms the standard SSD based
approach to the problem.
