The Market Generator

16 Pages Posted: 31 May 2019 Last revised: 28 Jan 2020

See all articles by Alexei Kondratyev

Alexei Kondratyev

Standard Chartered Bank

Christian Schwarz

affiliation not provided to SSRN

Date Written: May 8, 2019

Abstract

We propose to use a special type of generative neural networks - a Restricted Boltzmann Machine (RBM) - to build a powerful generator of synthetic market data that can replicate the probability distribution of the original market data. An RBM constructed with stochastic binary activation units in both the hidden and the visible layers (Bernoulli RBM) can learn complex dependence structures while avoiding overfitting. In this paper we consider an efficient data transformation and sampling approach that allows us to operate Bernoulli RBM on real-valued data sets and control the degree of autocorrelation and non-stationarity in the generated time series.

Keywords: Restricted Boltzmann Machine, non-parametric sampling, generation of market scenarios, complex dependence structures

JEL Classification: C63, G17

Suggested Citation

Kondratyev, Alexei and Schwarz, Christian, The Market Generator (May 8, 2019). Available at SSRN: https://ssrn.com/abstract=3384948 or http://dx.doi.org/10.2139/ssrn.3384948

Alexei Kondratyev (Contact Author)

Standard Chartered Bank ( email )

1 Basinghall Avenue
London, EC2V 5DD
United Kingdom

Christian Schwarz

affiliation not provided to SSRN

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