Neural networks can detect model-free static arbitrage strategies

• URL: http://arxiv.org/abs/2306.16422v2
• Date: Tue, 13 Aug 2024 10:06:36 GMT
• Title: Neural networks can detect model-free static arbitrage strategies
• Authors: Ariel Neufeld, Julian Sester,
• Abstract summary: We show that neural networks can detect model-free static arbitrage opportunities whenever the market admits some. 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.
• Score: 5.639904484784127
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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