Applications of Deep Learning to Financial Time Series Forecasting

Deep learning has recently risen as a dominant technique in a variety of settings comprising large-scale and high-dimensional data. In the particular case of financial modeling, one of the most important data analysis problems consists of predicting the future volatility of a given asset. In this thesis, we investigate how the Transformer architecture performs at the task of volatility forecasting by comparing its performance against that of previously explored deep learning architectures such as the LSTM.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Camelo Sa, Lucas

Advisor dc:contributor.advisor

• Kim, Yoon

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151390

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151390