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

TL;DR

This paper introduces a novel framework using machine learning models, specifically Boltzmann Machines and GANs, to generate realistic financial data for more robust backtesting of trading strategies, enhancing risk management.

Contribution

It proposes a new approach to simulate multi-dimensional financial time series that preserve key statistical properties, improving backtest reliability and risk assessment.

Findings

Synthetic data accurately replicates market statistical properties

Enhanced backtesting robustness demonstrated with proposed models

Framework supports better risk management in quantitative strategies

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.