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

• URL: http://arxiv.org/abs/2007.04838v1
• Date: Thu, 9 Jul 2020 14:37:45 GMT
• Title: Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks
• Authors: Edmond Lezmi, Jules Roche, Thierry Roncalli, Jiali Xu
• Abstract summary: 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. The article proposes then a new approach for estimating the probability distribution of backtest statistics.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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