Hybrid ARMA-GARCH-Neural Networks for intraday strategy exploration in high-frequency trading

• Published in: Pattern recognition Vol. 148; p. 110139
• Main Authors: Alaminos, David, Salas, M. Belén, Partal-Ureña, Antonio
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2024
• Subjects: Autoregressive moving average; Defence stock prices; FOREX markets; Generalized autoregressive conditional heteroskedasticity; High-frequency; Intraday trading; Neural networks; Quantum computing; Generalized autoregressive conditional heteroskedasticity; Quantum computing; Neural networks; FOREX markets; Autoregressive moving average; High-frequency; Intraday trading; Defence stock prices
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2023.110139

•This study examines the potential impact of global geopolitical events on the stock market prices of the Dow Jones U.S. Aerospace & Defense Index, as well as fluctuations in FOREX markets such as the British pound and Chinese renminbi.•We build an Autoregressive Moving Average Model with a Generalized Autoregressive Conditional Heteroskedasticity process (ARMA-GARCH) with the machine learning methods of Neural Networks, Deep Recurrent Convolutional Neural Networks, Deep Neural Decision Trees, Quantum Neural Networks, and Quantum Recurrent Neural Networks.•We aim at detecting intraday patterns for forecasting defense stock market and FOREX markets disturbances in a market microstructure framework. The empirical results provide findings on the foreseeability of market disturbances and small differences are observed before and during geopolitical events.•Additionally, we confirm the effectiveness of the hybrid model ARMA-GARCH with the machine learning approaches, being ARMA-GARCH-Quantum Recurrent Neural Network the technique that achieves the best accuracy results.•Our work has a large potential impact on investment market agents and portfolio managers, as shocks from geopolitical events could provide a new methodology to support the decision-making process for High-Frequency Trading. The frequency of armed conflicts increased during the last 20 years. The problems of the emergence of military disputes, not only concern social parameters, but also economic and financial dimensions. This study examines the potential impact of global geopolitical events on the stock market prices of the Dow Jones U.S. Aerospace & Defense Index and Foreign Exchange (FOREX) markets movements. We analyse whether defence stocks and exchange rate perform similarly during military incidents or geopolitical crises. We built an Autoregressive Moving Average Model with a Generalized Autoregressive Conditional Heteroskedasticity process (ARMA-GARCH) with the machine learning methods of Neural Networks, Deep Recurrent Convolutional Neural Networks, Deep Neural Decision Trees, Quantum Neural Networks, and Quantum Recurrent Neural Networks, aimed at detecting intraday patterns for forecasting defence stock market and FOREX markets disturbances in a market microstructure framework. The empirical results provide preliminary findings on the foreseeability of market disturbances and small differences are observed before and during geopolitical events. Additionally, we confirm the effectiveness of the hybrid model ARMA-GARCH with the machine learning approaches, being ARMA-GARCH-Quantum Recurrent Neural Network the technique that achieves the best accuracy results. Our work has a large potential impact on investment market agents and portfolio managers, as shocks from geopolitical events could provide a new methodology to support the decision-making process for trading in High-Frequency Trading.