  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.
