Pricing Financial Derivatives with Multi-Task Learning

Abstract

This paper reviews machine learning methods on forecasting financial data. Although many authors such as (Hutchinson et. al) has explored this topic intensely, their methods ignore possible interrelations amongst different group of securities with related price dynamics. Thus, we would like to further exploit such possible relationships and improve upon current methods by introducing multi-task machine learning tools. In addition, we will reformulate our approach as a Gaussian mixed effects model in order to find confidence intervals and employ prior distributions. Our data set will be the closing prices of 5 stocks in the Dow Jones Index. Our machine learning models show only a slight improvement to baseline linear models, but promising results for option pricing.