Cross-Market Arbitrage Strategies Based on Deep Learning
DOI:
https://doi.org/10.5281/zenodo.12747401ARK:
https://n2t.net/ark:/40704/AJSM.v2n4a04Keywords:
Cross-market Arbitrage, Deep Learning, LSTM, CNN, Reinforcement Learning, Data Quality, Risk Management, Financial MarketsAbstract
Cross-market arbitrage involves exploiting price differences of the same or similar financial instruments across different markets. The advent of deep learning (DL) has introduced new avenues for developing sophisticated arbitrage strategies. This paper explores how DL can be leveraged to enhance cross-market arbitrage strategies, focusing on the potential benefits, challenges, and practical applications. Through a comprehensive review of current literature and empirical case studies, we aim to provide insights into the integration of DL in arbitrage strategies, highlighting its impact on market efficiency and profitability. By examining DL techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and reinforcement learning (RL), this study aims to demonstrate how these advanced methods can optimize arbitrage opportunities, manage risks, and improve overall trading performance in dynamic financial markets.
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