Mixed Tabu machine for portfolio optimization problem

ABSTRACT

In this paper, we introduce a novel artificial neural network (NN) to solve the portfolio optimization problem. The proposed NN is called the Mixed Tabu Machine (MTM) since its structure is similar to the Tabu Machine, but includes both discrete and continuous variables. Similar to the Hopfield network, the state of the MTM is updated to find the global minimum energy state. To escape from local minimum states of the energy in the MTM, the state transition mechanism is controlled by a Tabu search in both discrete and continuous search spaces. The experimental results for five standard benchmark data sets show that the MTM can clearly obtain good results in very small computation time.

KEYWORDS:

• Tabu Machine
• Hopfield network
• portfolio optimization problem

2010 AMS SUBJECT CLASSIFICATIONS:

• 68T20
• 91G10

Disclosure statement

No potential conflict of interest was reported by the authors.