Using Constraint Programming to Design an Option‐based Decision Support System

Financial options and futures give investors the opportunity to form complex strategies that meet their investment objectives for risk management. However, this opportunity gives rise to a difficult task: finding a desired strategy from among a large number of possible strategies. This paper describes an intelligent decision‐support system for generating option‐based investment strategies by using constraint programming, which is an integrated framework of Artificial Intelligence and Logic Programming. In this system, constraint programming acts as a bridge between qualitative and quantitative analyses in decision processes. In qualitative analysis, logical reasoning with hypotheses is used to automatically create complex strategies through abstract matching with investors' profiles. Here, abstract matching can be regarded as symbolic computation for producing qualitatively reasonable strategies. In quantitative analysis, a set of complete solutions that satisfy user‐supplied constraints are obtained by constraint satisfaction and optimization. A constraint language based on the framework of Constraint Logic Programming has been developed in order to integrate these symbolic and numerical computations in a uniform way. The resulting system written in this language has the following features. (1) Unlike rule‐based expert systems, the constraint‐based system can create novel investment strategies. (2) A smooth transition from qualitative to quantitative analyses can be naturally achieved due to the constraint language. (3) Qualitative analysis can reduce search complexity, because the analysis focuses on a small set of qualitative distinctions in solution space. These features indicate the usefulness of constraint programming for designing intelligent decision‐support systems.