Deep Learning for Finance: Deep Portfolios

Applied Stochastic Models in Business and Industry 33 (1), 3-12.

15 Pages Posted: 14 Sep 2016 Last revised: 29 Apr 2019

See all articles by J.B. Heaton

J.B. Heaton

J.B. Heaton, P.C.

Nick Polson

University of Chicago - Booth School of Business

Jan Witte

University College London - Department of Mathematics

Date Written: September 5, 2016

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.

Keywords: Deep Learning, Machine Learning, Big Data, Artificial Intelligence, Finance, Asset Pricing, Volatility, Deep Frontier

JEL Classification: C58, G11, G12

Suggested Citation

Heaton, J.B. and Polson, Nick and Witte, Jan, Deep Learning for Finance: Deep Portfolios (September 5, 2016). Applied Stochastic Models in Business and Industry 33 (1), 3-12.. Available at SSRN: https://ssrn.com/abstract=2838013 or http://dx.doi.org/10.2139/ssrn.2838013

J.B. Heaton

J.B. Heaton, P.C. ( email )

20 West Kinzie
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HOME PAGE: http://jbheaton.com

Nick Polson

University of Chicago - Booth School of Business ( email )

5807 S. Woodlawn Avenue
Chicago, IL 60637
United States
773-702-7513 (Phone)
773-702-0458 (Fax)

Jan Witte (Contact Author)

University College London - Department of Mathematics ( email )

Gower Street
London, WC1E 6BT
United Kingdom

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