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 nancial markets with a high number of traded securities and ensures almost immediate execution of the corresponding trading strategies. To demonstrate its tractability, e ectiveness, and robustness we provide examples using real nancial data. From a technical point of view, we prove that a single neural network can approximately solve a class of convex semi-in nite 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 nancial market admits such opportunities.
Can be found here
IMPORTANT: Since the original *.csv-files contain data downloaded from Bloomberg, we can unfortunately NOT provide them here. The *.csv files are therfore just DUMMY-files showcasing the structure of the used data.
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The Notebook to download the real option data of SP 500 Constituents.
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The Notebook containing Algorithm 1 (training of the neural network) applied to real data.
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The Notebook covering the backtesting procedure on historical data. (HISTORICAL OPTION DATA CAN UNFORTUNATELY NOT BE PROVIDED)
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The Notebook covering the backtesting of the trained neural network on the test data.
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The trained neural network .
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