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dc.contributor.author Chan, Adrian
dc.date.accessioned 2012-04-25T23:00:18Z
dc.date.available 2012-04-25T23:00:18Z
dc.date.issued 2012-04-25
dc.identifier.uri http://hdl.handle.net/10161/5229
dc.description Honors thesis en_US
dc.description.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. en_US
dc.language.iso en_US en_US
dc.subject Machine Learning en_US
dc.subject Options Pricing en_US
dc.subject Multi-Task Learning en_US
dc.title Pricing Financial Derivatives with Multi-Task Learning en_US
dc.department Mathematics en_US

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