Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks

Abstract

This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies, in particular in the space of smart beta, factor investing and alternative risk premia.

Keywords: Machine learning, generative approach, discriminative approach, restricted Boltzmann machine, generative adversarial network, Wasserstein distance, market generator, quantitative asset management, backtesting, trading strategy

JEL Classification: C53, G11