Neural networks can detect model-free static arbitrage strategies

Ariel Neufeld and Julian Sester

Papers from arXiv.org

Abstract: 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.

Date: 2023-06, Revised 2024-08
New Economics Papers: this item is included in nep-big, nep-cmp and nep-net
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