A Markov Decision Process Model for Optimal Trade of Options Using Statistical Data

This paper presents a Markov decision process model for calculating optimal decision policy regarding the trade of options assuming the American options trading system. The proposed model incorporates the conditional probabilities of option prices given various features (or factors) that affect those prices. The generation of such probabilities requires statistical data of the feature values as well as the option price values. Given the availability of statistical data, the paper explains how the Markov decision process model can be formulated and solved using ‘value iteration’ to calculate optimal decision policy that maximizes the accumulative return. The model has been applied to the data of Microsoft and Coca Cola options. Analysis in the case study reveals how optimal decision policy can be interpreted and used for making sales or purchase decisions regarding various options at hand. The results indicate that there are significant advantages for the financial community including, but not limited to the investors who utilize our proposed approach.