风向标交易:对预测市场未来价格走势有影响的交易特征

Tejas Ramdas, Martin T. Wells
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引用次数: 0

摘要

在本研究中,我们利用强大的非线性机器学习方法来识别包含有价值信息的交易特征。首先,我们展示了优化神经网络预测器在准确预测未来市场走势方面的有效性。然后,我们利用这个成功的神经网络预测器所提供的信息,找出每个数据点(交易窗口)中对优化神经网络预测未来价格走势影响最大的单个交易。这种方法有助于我们揭示不同规模、不同场所、不同交易环境和不同时间的交易所提供的信息内容的异质性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets
In this study, we leverage powerful non-linear machine learning methods to identify the characteristics of trades that contain valuable information. First, we demonstrate the effectiveness of our optimized neural network predictor in accurately predicting future market movements. Then, we utilize the information from this successful neural network predictor to pinpoint the individual trades within each data point (trading window) that had the most impact on the optimized neural network's prediction of future price movements. This approach helps us uncover important insights about the heterogeneity in information content provided by trades of different sizes, venues, trading contexts, and over time.
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