机器学习增强型配对交易

Eli Hadad, Sohail Hodarkar, Beakal Lemeneh, Dennis Shasha
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引用次数: 0

摘要

由于价格变化类似于白噪声,因此预测金融市场的收益是一项众所周知的挑战。在本文中,我们提出了应对这一挑战的新方法。我们采用巴西股票市场全年一分钟粒度的高频数据,应用各种统计和机器学习算法,包括 ARIMA、双向长短期记忆(BiLSTM)、Transformers、N-BEATS、N-HiTS、卷积神经网络(CNN)和时序卷积网络(TCN),预测密切相关股票对的价格比率变化。我们的研究结果表明,将还原法和基于机器学习的预测方法相结合,可获得最高的单笔交易利润。此外,通过允许模型在预测变化幅度较小时放弃交易,每笔交易的利润可以进一步提高。我们提出的预测方法利用了多种方法的混合,在高频数据方面比单个方法具有更高的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-Enhanced Pairs Trading
Forecasting returns in financial markets is notoriously challenging due to the resemblance of price changes to white noise. In this paper, we propose novel methods to address this challenge. Employing high-frequency Brazilian stock market data at one-minute granularity over a full year, we apply various statistical and machine learning algorithms, including ARIMA, Bidirectional Long Short-Term Memory (BiLSTM) with attention, Transformers, N-BEATS, N-HiTS, Convolutional Neural Networks (CNNs), and Temporal Convolutional Networks (TCNs) to predict changes in the price ratio of closely related stock pairs. Our findings indicate that a combination of reversion and machine learning-based forecasting methods yields the highest profit-per-trade. Additionally, by allowing the model to abstain from trading when the predicted magnitude of change is small, profits per trade can be further increased. Our proposed forecasting approach, utilizing a blend of methods, demonstrates superior accuracy compared to individual methods for high-frequency data.
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