Time-Varying Mean-Variance Portfolio Selection under Transaction Costs and Cardinality Constraint Problem via Beetle Antennae Search Algorithm (BAS)

The Markowitz mean-variance portfolio selection is widely acclaimed as a very important investment strategy. A popular option to solve the static (MVPS) problem based on use of quadratic programming (QP) methods. On other hand, under transaction costs (PSTC) usually approached with nonlinear (NLP) In this article, we define and study time-varying cardinality constraint (TV-MVPSTC-CC) (TVNLP) problem. TV-MVPSTC-CC also comprises properties moving average. These make an even greater analysis tool suitable evaluate investments identify trading opportunities across continuous-time period. Using Beetle Antennae Search (BAS) algorithm, provide online solution NLP To best our knowledge, innovative approach that incorporates modern meta-heuristic optimization techniques online, thus more realistic, way, present financial while eliminating restrictions Our verified by numerical experiments computer simulations excellent alternative traditional approaches.