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.
Type
Honors thesisDepartment
MathematicsPermalink
https://hdl.handle.net/10161/5229Citation
Chan, Adrian (2012). Pricing Financial Derivatives with Multi-Task Learning. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/5229.Collections
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