Detecting data-driven robust statistical arbitrage strategies with deep neural networks

TL;DR

This paper introduces a deep neural network-based approach for identifying robust statistical arbitrage strategies in high-dimensional financial markets, effective even during crises and when traditional cointegration fails.

Contribution

The novel methodology enables model-free, data-driven detection of arbitrage strategies without relying on cointegration, suitable for high-dimensional and complex markets.

Findings

Achieved profitable trading strategies in 50-dimensional markets.

Effective during financial crises and cointegration breakdowns.

Demonstrated robustness and high performance of the approach.

Abstract

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.