Financial Data Mining with Genetic Programming: a Survey and Look Forward

First Published Thursday, 1 January 1970 12:34 am from Automated Trader

Genetic Programming (GP) is an appealing machine-learning technique for tackling financial engineering problems: it belongs to the family of evolutionary algorithms that have proven to be remarkably successful at handling complex optimization problems, and possesses the unique feature of producing solutions under a symbolic form that can be understood and analyzed by humans. Over the last decade, GP has been applied to generate financial trading strategies, forecast stocks and options prices, or grasp some insight into the dynamics of the markets and the behavior of the agents. In this paper, we first provide a brief survey of the existing studies, then highlight fields of investigations that, we believe, should lead to enhance the applicability and efficiency of GP in the financial domain. By Nicolas NAVET and Shu-Heng CHEN