A Soft Computing Approach to Enhanced Indexation.

We propose an integrated and interactive procedure for designing an enhanced indexation strategy with predetermined investment goals and risk constraints. It is based on a combination of soft computing techniques for dealing with practical and computation aspects of this problem. We deviate from the main trend in enhanced indexation by considering a) restrictions on the total number of tradable assets and b) non-standard investment objectives, focusing e.g. on the probability that the enhanced strategy under-performs the market. Fuzzy set theory is used to handle the subjectivity of investment targets, allowing a smooth variation in the degree of fulfilment with respect to the value of performance indicators. To deal with the inherent complexity of the resulting cardinality-constraint formulations, we apply three nature-inspired optimisation techniques: simulated annealing, genetic algorithms and particle swarm optimisation. Optimal portfolios derived from "soft" optimisers are then benchmarked against the American Dow Jones Industrial Average (DJIA) index and two other simpler heuristics for detecting good asset combinations: a Monte Carlo combinatorial optimisation method and an asset selection technique based on the capitalisation and the beta coefficients of index member stocks.