Neural networks can detect model-free static arbitrage strategies
This addresses the challenge of quickly identifying arbitrage strategies in high-dimensional financial markets, though it appears incremental as it applies neural networks to an existing problem.
The paper tackles the problem of detecting model-free static arbitrage opportunities in financial markets, demonstrating theoretically and numerically that neural networks can identify these opportunities when they exist, with examples using real financial data showing tractability and robustness.
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.