Asset selection in global financial markets using genetic network programming

Asset selection is a challenging task in the complex global financial system, whose nature has highlighted the need to rethink conventional practices. The attractive and non-toxic assets must be kept on the eye so that our financial systems sustain building blocks in our economic systems. This paper presents an asset selection framework using Genetic Network Programming(GNP). GNP handles evolvable graph structures that prevent the size expansion for dynamic and complex environments, which in turn make it suitable for dealing with decision processes effectively under uncertainty such as partially observable Markov decision processes. Simulations using stocks, bonds and currencies from relevant financial markets in USA, Europe and Asia show the competitive advantages of the proposed method against relevant selection strategies in the finance literature.