Hybrid Ridgelet Deep Neural Networks for Data-Driven Arbitrage Strategies

Abstract

In this study, we propose a novel model framework that integrates deep neural networks with the Ridgelet Transform. The Ridgelet Transform on Borel measurable functions is used for arbitrage detection on high-dimensional sparse structures. This transform also enhances the expressive power of neural networks, enabling them to capture complex and high-dimensional market structures. Theoretically, we determine profitable trading strategies by optimizing hybrid ridgelet deep neural networks. Further, we emphasize the role of activation functions in ensuring stability and adaptability under uncertainty. We use a high-performance computing cluster for the detection of arbitrage across multiple assets, ensuring scalability, and processing large-scale financial data. Empirical results demonstrate strong profitability across diverse scenarios involving up to 50 assets, with particularly robust performance during periods of market volatility.