An Improved Quick Artificial Bee Colony Algorithm for Portfolio Selection

Computation Intelligence has inspired many researchers to develop the capability of computers to learn and solve a complex task in real-world problems. In this work, we propose an Artificial Bee Colony (ABC) to deal with the Stock Selection problem. We apply a Sigmoid-based Discrete-Continuous model with ABC to select appropriate features for stock scoring. The empirical study tests the performance of ABC compared with Genetic Algorithm (GA) and Differential Evolution (DE) algorithm by using data from the Stock Exchange Thailand. The empirical results show that the novel model stock selection significantly outperforms in terms of both investment return, diversity and model robustness.