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

arXiv: https://arxiv.org/abs/2007.04838 · HTML: https://mlsanity.com/paper/2007.04838/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [Scenario generation for market risk models using generative neural networks](https://mlsanity.com/paper/2109.10072/) (2021)
- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)
- [Discriminating modelling approaches for Point in Time Economic Scenario Generation](https://mlsanity.com/paper/2108.08818/) (2021)

## Abstract (arXiv)

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.