A constraint-oriented decision support system for option-based investment strategies

With the advent of options and futures on financial instruments, investors have the opportunity to form complex strategies that meeting their investment objectives. However, this opportunity gives rise to a difficult task for finding a desired strategy among a large amount of investment strategies. The authors describe an intelligent decision-support system for generating option-based investment strategies by using the notion of constraint satisfaction, which is widely applied to search problems. In this system, constraint satisfaction plays a role of navigation for automatically creating complex strategies through abstract matching with investors´ profiles. Here, abstract matching can be viewed as a search method for producing qualitatively reasonable strategies, that describe a set of options to buy or sell without numerical information. Because this technique can be used as preprocessing to quantitative analysis such as linear programming in order to obtain an optimal solution, the proposed system provides a bridge for smooth transition between qualitative and quantitative analysis