Generative Adversarial Neural Networking of Agents: Avatars as Tools for Financial Modelling

Generative Adversarial Neural Networking of Agents: Avatars as Tools for Financial Modelling

Vladimir Soloviev (Financial University Under the Government of the Russian Federation, Russia), Vsevolod Chernyshenko (Financial University Under the Government of the Russian Federation, Russia), Vadim Feklin (Financial University Under the Government of the Russian Federation, Russia), Ekaterina Zolotareva (Financial University Under the Government of the Russian Federation, Russia) and Nikita Titov (Financial University Under the Government of the Russian Federation, Russia)
DOI: 10.4018/978-1-7998-1581-5.ch005

Abstract

The chapter is devoted to the problem of analytical analysis of implementation of generative-competitive neural networks in predicting the state of financial markets (particularly to predict future moments of changing market conditions) based on the use of convolutional and generative neural networks, as well as reinforcement training. An algorithm for predicting future moments of trend change under concrete market conditions based on generative adversarial networks was developed. Special software that realizes algorithms for predicting future moments of changing market conditions, based on the algorithms mentioned above was designed.
Chapter Preview
Top

Background

The idea of generative-adversarial networks (GANs) was proposed by J. Goodfellow of the University of Montreal (Goodfellow et al. 2014). During literally a couple of years, this method has found its application in the tasks of semantic image segmentation, medical information analysis, material recognition, time series analysis (Luc et al. 2016), (Che et al. 2017), (Erickson et al. 2017), (Esteban et al. 2017), (Chen et al. 2016), (Hinton, Salakhutdinov 2006), (Mescheder et al. 2017), (Dumoulin et al. 2016), (Donahue et al. 2016), (Li et al. 2017), (Reed et al. 2016), (Isola et al. 2016), (Ledig et al. 2016), (Ren et al. 2015), (Lee et al. 2016).

Complete Chapter List

Search this Book:
Reset