Analyzing Deep Generated Financial Time Series for Various Asset Classes

50 Pages Posted: 30 Aug 2021

See all articles by Antonio Rosolia

Antonio Rosolia

Zurich University of Applied Sciences

Joerg Osterrieder

University of Twente; Bern Business School

Date Written: August 3, 2021

Abstract

Generative Adversarial Networks (GANs) have shown remarkable success as a framework for trainingmodels to produce realistic-looking data. In this work, we propose a GAN to produce realistic real-valued time series, with an emphasis on their application to financial data.Our aim is having a GAN, applied on various financial time series for various asset classes, that canreflect all the characteristics of them, as well as the characteristics we may be unaware of, as GANslearn the underlying structure of our data, rather than just a set of features. If we are able to achievethis, the synthetic datasets we create could be used for a variety of purposes including model trainingand model selection. In this paper we try to train a GAN with real data, representing one asset ofdifferent asset classes such as commodities, forex, futures, index and shares.

Keywords: Time Series Classification, Generative Adversarial Networks, Financial Time Series

Suggested Citation

Rosolia, Antonio and Osterrieder, Joerg, Analyzing Deep Generated Financial Time Series for Various Asset Classes (August 3, 2021). Available at SSRN: https://ssrn.com/abstract=3898792 or http://dx.doi.org/10.2139/ssrn.3898792

Antonio Rosolia (Contact Author)

Zurich University of Applied Sciences ( email )

Technikumstrasse 9
Winterthur, Zurich 8401
Switzerland

Joerg Osterrieder

University of Twente ( email )

Drienerlolaan 5
Departement of High-Tech Business and Entrepreneur
Enschede, 7522 NB
Netherlands

Bern Business School ( email )

Brückengasse
Institute of Applied Data Sciences and Finance
Bern, BE 3005
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
365
Abstract Views
1,134
Rank
168,960
PlumX Metrics