We present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets. Robust statistical arbitrage strategies refer to trading strategies that enable profitable trading under model ambiguity. The presented novel methodology allows to consider a large amount of underlying securities simultaneously and does not depend on the identification of cointegrated pairs of assets, hence it is applicable on high-dimensional financial markets or in markets where classical pairs trading approaches fail. Moreover, we provide a method to build an ambiguity set of admissible probability measures that can be derived from observed market data. Thus, the approach can be considered as being model-free and entirely data-driven. We showcase the applicability of our method by providing empirical investigations with highly profitable trading performances even in 50 dimensions, during financial crises, and when the cointegration relationship between asset pairs stops to persist.

翻译：我们提出了一种基于深度神经网络的方法，用于识别金融市场中具有鲁棒性的统计套利策略。鲁棒统计套利策略指在模型不确定性的情况下仍能实现盈利交易的策略。该新方法能同时考虑大量基础证券，且不依赖于协整资产对的识别，因此适用于高维金融市场或经典配对交易策略失效的市场。此外，我们提供了一种从观测市场数据构建可容许概率测度模糊集的方法，使该算法可视为无模型且完全数据驱动。我们通过实证研究展示了方法的适用性：即使在50维金融市场、金融危机期间以及资产间协整关系失效时，该方法仍能获得高盈利性的交易表现。