Detecting data-driven robust statistical arbitrage strategies with deep neural networks
This addresses the challenge of profitable trading under model ambiguity in high-dimensional financial markets, offering a novel data-driven approach that is not incremental but provides a new methodology.
The authors tackled the problem of identifying robust statistical arbitrage strategies in financial markets using deep neural networks, achieving highly profitable trading performances even in 50 dimensions and during financial crises.
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.