Modeling expert knowledge using 'situation-action' rules is not always feasible in knowledge intensive involving volatile knowledge. In such domains, the size and the dynamic nature of the search space make it extremely difficult to setup a rule base and keep it accurate. An alternative approach suggests that in some domains many of the rules that experts use can be derived by reasoning from 'first-principles'. That approach entails modeling experts' deep knowledge, and emulating the reasoning processes with deep knowledge that allow experts to derive many rules and their justification. This paper discusses the design and implementation of an object-oriented representation for the deep knowledge traders utilize in a business domain called hedging, which is knowledge intensive and involves volatile knowledge. It illustrates how deep knowledge modeled using that representation is used to support reasoning from first-principles. The paper also analyzes features of that representation that were found to be beneficial in the development of a knowledge-based system called INTELLIGENT-HEDGER.