  Given a random variable $O \in \mathbb{R}$ and a set of experts $E$, we
describe a method for finding a subset of experts $S \subseteq E$ whose
aggregated opinion best predicts the outcome of $O$. Therefore, the problem can
be regarded as a team formation for performing a prediction task. We show that
in case of aggregating experts' opinions by simple averaging, finding the best
team (the team with the lowest total error during past $k$ turns) can be
modeled with an integer quadratic programming and we prove its NP-hardness
whereas its relaxation is solvable in polynomial time. Finally, we do an
experimental comparison between different rounding and greedy heuristics and
show that our suggested tabu search works effectively.
  Keywords: Team Selection, Information Aggregation, Opinion Pooling, Quadratic
Programming, NP-Hard
