论文标题
通过深度神经网络检测数据驱动的稳健统计套利策略
Detecting data-driven robust statistical arbitrage strategies with deep neural networks
论文作者
论文摘要
我们提出了一种基于深层神经网络的方法，该方法允许确定金融市场中强大的统计套利策略。强大的统计套利策略是指在模型歧义下实现盈利交易的交易策略。提出的新方法允许同时考虑大量基础证券，并且不依赖于协调对资产的识别，因此它适用于高维金融市场或经典交易方法失败的市场。此外，我们提供了一种方法来构建一套可以从观察到的市场数据得出的可接受概率措施集。因此，该方法可以视为无模型，并且完全由数据驱动。我们通过在50个维度，金融危机期间以及资产配对之间的协整关系停止持续存在时，即使在50个维度，即使在50个方面，也可以在50个维度上提供高利润的交易表现来展示我们的方法的适用性。
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.
