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Pricing Financial Derivatives with Multi-Task Learning

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Date
2012-04-25
Author
Chan, Adrian
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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 thesis
Department
Mathematics
Subject
Machine Learning
Options Pricing
Multi-Task Learning
Permalink
https://hdl.handle.net/10161/5229
Citation
Chan, Adrian (2012). Pricing Financial Derivatives with Multi-Task Learning. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/5229.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

Rights for Collection: Undergraduate Honors Theses and Student papers


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