Sample Mapping\
from worst to best & Scheme\
1st & 15\
2nd & 12\
3rd & 9\
4th & 6\
5th & 3\
6th & 0\
7th & $-$3\
8th & $-$6\
9th & $-$9\
10th & $-$12\
11th & $-$15\

[@\*3c@]{} &\
& Sample Mapping\
& Scheme\
$-1.00 \leq \rho_{ij}    < -0.25$ & $-$5\
$-0.25 \leq \rho_{ij}    < -0.15$ & $-$3\
$-0.15 \leq \rho_{ij}    < -0.05$ & $-$1\
$-0.05 \leq \rho_{ij}    <  0.05$ & 0\
$ 0.05 \leq \rho_{ij}    <  0.15$ & 1\
$ 0.15 \leq \rho_{ij}    <  0.25$ & 3\
$ 0.25 \leq \rho_{ij} \leq  1.00$ & 5\

The instance set used for our case study is obtained by simulating asset values with the help of correlated Geometric Brownian Motion (GBM) processes with uniform constant asset correlation $\rho$, drift $\mu$ and log-normal asset volatility $\sigma$. The specific values of these parameters were derived from a wide range of fund industry researches (see, e.g., [@darolles2010conditionally] for the Sharpe ratio distributions) and, therefore, can be viewed as representative for the industry. The simulation time horizon was chosen to be 1 year and the time step was set at 1 month $-$ this setup corresponds to the situation where a Fund 