Farhat Iqbal, Dimitrios Koutmos, Eman A. Ahmed, Lulwah M. Al-Essa
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引用次数: 0
摘要
全球外汇(FX)市场是我们金融体系中一个重要而庞大的组成部分。在这个市场上,企业和投资者既进行投机交易,也进行套期保值。多年来,人们对外汇建模和预测的兴趣与日俱增。最近,机器学习(ML)和深度学习(DL)技术在提高预测准确性方面取得了可喜的成果。在外汇市场规模不断扩大以及 ML 技术不断进步的推动下,我们提出了一个新颖的预测框架,即 MVO-BiGRU 模型,该模型集成了变模分解(VMD)、数据增强、Optuna 优化超参数和双向 GRU 算法,用于月度外汇汇率预测。预防模块中的数据扩增大大增加了数据组合的多样性,有效地减少了过拟合问题,而 Optuna 优化则确保了模型配置的最优化,从而提高了性能。我们的研究成果包括 MVO-BiGRU 模型的开发,以及将其应用于外汇市场所获得的启示。我们的研究结果表明,MVO-BiGRU 模型可以成功避免过度拟合,并在样本外预测方面达到最高准确度,同时在多个评估标准方面优于基准模型。这些发现为在低频时间序列数据上实施 ML 和 DL 模型提供了有价值的见解,因为在低频时间序列数据上,人工数据增强可能具有挑战性。
A Novel Hybrid Deep Learning Method for Accurate Exchange Rate Prediction
The global foreign exchange (FX) market represents a critical and sizeable component of our financial system. It is a market where firms and investors engage in both speculative trading and hedging. Over the years, there has been a growing interest in FX modeling and prediction. Recently, machine learning (ML) and deep learning (DL) techniques have shown promising results in enhancing predictive accuracy. Motivated by the growing size of the FX market, as well as advancements in ML, we propose a novel forecasting framework, the MVO-BiGRU model, which integrates variational mode decomposition (VMD), data augmentation, Optuna-optimized hyperparameters, and bidirectional GRU algorithms for monthly FX rate forecasting. The data augmentation in the Prevention module significantly increases the variety of data combinations, effectively reducing overfitting issues, while the Optuna optimization ensures optimal model configuration for enhanced performance. Our study’s contributions include the development of the MVO-BiGRU model, as well as the insights gained from its application in FX markets. Our findings demonstrate that the MVO-BiGRU model can successfully avoid overfitting and achieve the highest accuracy in out-of-sample forecasting, while outperforming benchmark models across multiple assessment criteria. These findings offer valuable insights for implementing ML and DL models on low-frequency time series data, where artificial data augmentation can be challenging.