Deep Learning in Finance
This addresses prediction challenges in finance, but it is incremental as it applies existing deep learning methods to a new domain.
The paper tackles financial prediction problems like pricing securities and risk management by applying deep learning hierarchical models to large datasets with complex interactions, resulting in more useful outcomes than standard methods by detecting interactions invisible to existing economic theory.
We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems -- such as those presented in designing and pricing securities, constructing portfolios, and risk management -- often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.