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

Researchers from the division of Mathematical Sciences at Nanyang Technological University in Singapore Ariel Neufeld, Julian Sester and Daiying Yin present an approach, based on deep neural networks, that allows identifying robust statistical arbitrage strategies in financial markets.

Robust statistical arbitrage strategies refer to self-financing trading strategies that enable profitable trading under model ambiguity. The presented novel methodology does not suffer from the curse of dimensionality nor does it depend on the identification of cointegrated pairs of assets and is therefore applicable even on high-dimensional financial markets or in markets where classical pairs trading approaches fail.

Moreover, they 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. They showcase the applicability of the 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.


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