Deep Learning in Finance

TL;DR

This paper discusses how deep learning hierarchical models can improve financial prediction and classification tasks by capturing complex data interactions that traditional models cannot effectively handle.

Contribution

It introduces the application of deep learning to finance, highlighting its ability to uncover hidden data interactions and enhance predictive accuracy.

Findings

Deep learning models outperform standard financial methods.

They can detect complex, invisible data interactions.

Improved accuracy in security pricing and risk management.

Abstract

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.