Cardinality constrained portfolio selection via factor models

TL;DR

This paper introduces new 0-1 linear and quadratic models, including a piecewise linear approximation and a heuristic, to efficiently solve cardinality constrained portfolio selection problems using factor models.

Contribution

It presents novel 0-1 and quadratic models with approximations and heuristics for cardinality constrained portfolios, improving computational efficiency.

Findings

0-1 models outperform traditional methods in computation time

Piecewise linear approximation reduces solution complexity

Heuristic provides fast solutions for single factor models

Abstract

In this paper we propose and discuss different 0-1 linear models in order to solve the cardinality constrained portfolio problem by using factor models. Factor models are used to build portfolios to track indexes, together with other objectives, also need a smaller number of parameters to estimate than the classical Markowitz model. The addition of the cardinality constraints limits the number of securities in the portfolio. Restricting the number of securities in the portfolio allows us to obtain a concentrated portfolio, reduce the risk and limit transaction costs. To solve this problem, a pure 0-1 model is presented in this work, the 0-1 model is constructed by means of a piecewise linear approximation. We also present a new quadratic combinatorial problem, called a minimum edge-weighted clique problem, to obtain an equality weighted cardinality constrained portfolio. A piecewise linear approximation for this problem is presented in the context of a multi factor model. For a single factor model, we present a fast heuristic, based on some theoretical results to obtain an equality weighted cardinality constraint portfolio. The consideration of a piecewise linear approximation allow us to reduce significantly the computation time required for the equivalent quadratic problem. Computational results from the 0-1 models are compared to those using a state-of-the-art Quadratic MIP solver.