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ND-SMPF: A Noisy Deep Neural Network Fusion Framework for Stock Price Movement Prediction

机译：ND-SMPF：用于股价运动预测的嘈杂的深度神经网络融合框架

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摘要

There has been a recent surge of interest on development of news-oriented Deep Neural Network (DNN) architectures to predict stock trend movements. Limited focus is, however, devoted to reliability fusing different available information resources. In this regard, this paper proposes a Noisy Deep Stock Movement Prediction Fusion framework (ND-SMPF) for stock price movement prediction. The proposed ND-SMPF predictive framework uses information fusion to combine twitter data with extended horizon market historical prices to boost the accuracy of the stock movement prediction task. More specifically, Noisy Bi-directional Gated Recurrent Unit (NBGRU) is utilized coupled with a Hybrid Attention Network (HAN) to extract news level temporal information. A two level attention layer is used to identify relevant words with highest correlation and effects on the stock trends, which are then fused with historical price data to perform the prediction task. A real dataset is incorporated to evaluate performance of the proposed ND-SMPF framework, which illustrates superior performance in comparison to its recently developed counterparts.
机译：最近，对于开发面向新闻的深度神经网络（DNN）架构以预测股票趋势运动的兴趣激增。但是，将注意力集中在融合不同可用信息资源的可靠性上。在这方面，本文提出了一种用于股票价格走势预测的噪声深层股票走势预测融合框架（ND-SMPF）。拟议的ND-SMPF预测框架使用信息融合将Twitter数据与扩展的水平市场历史价格相结合，以提高股票移动预测任务的准确性。更具体地说，将噪声双向门控循环单元（NBGRU）与混合注意网络（HAN）结合使用以提取新闻级别的时间信息。二级注意层用于识别相关性最高的单词，并对词汇趋势产生影响，然后将其与历史价格数据融合以执行预测任务。合并了一个真实的数据集以评估提议的ND-SMPF框架的性能，该框架说明了与最近开发的同类产品相比更出色的性能。

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