Deep Learning in Finance

J.B. Heaton · Nicholas G. Polson · Jan Hendrik Witte

87 citations0/0 files storedarXiv (Cornell University)

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Stock Market Forecasting Methods Neural Networks and Applications Forecasting Techniques and Applications

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.

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References

1982Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation 1992The Cross-Section of Expected Stock Returns 2006Market Reactions to Tangible and Intangible Information 1994A Nonparametric Approach to Pricing and Hedging Derivative Securities Via Learning Networks

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