论文标题
通过Boltzmann机器和生成的对抗网络改善交易策略的鲁棒性
Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks
论文作者
论文摘要
本文探讨了机器学习模型来建立市场生成器。基本的想法是模拟人工多维财务时间序列，其统计属性与金融市场中观察到的属性相同。特别是，这些综合数据必须保留资产回报的概率分布，不同资产之间的随机依赖性和随时间的自相关。然后，本文提出了一种估计回测统计概率分布的新方法。最终目标是开发一个框架来改善定量投资策略的风险管理，尤其是在智能beta，因素投资和替代风险溢价的空间中。
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.
