Neural networks can detect model-free static arbitrage strategies
Ariel Neufeld Julian Sester
In this paper we demonstrate both theoretically as well as numerically that
neural networks can detect model-free static arbitrage opportunities whenever
the market admits some. Due to the use of neural networks, our method can be
applied to financial markets with a high number of traded securities and
ensures almost immediate execution of the corresponding trading strategies. To
demonstrate its tractability, effectiveness, and robustness we provide examples
using real financial data. From a technical point of view, we prove that a
single neural network can approximately solve a class of convex semi-infinite
programs, which is the key result in order to derive our theoretical results
that neural networks can detect model-free static arbitrage strategies whenever
the financial market admits such opportunities.