论文标题
Dual-CLVSA：一种新颖的深度学习方法，可以通过情感测量来预测金融市场
Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements
论文作者
论文摘要
预测金融市场是一项具有挑战性的任务。这项任务的复杂性主要是由于金融市场与市场参与者之间的相互作用，他们无法一直保持理性，并且经常受到诸如恐惧和狂喜之类的情绪的影响。基于最先进的方法，特别是金融市场预测，一种基于关注的混合卷积卷积基于LSTM的变化序列到序列模型（CLVSA），我们提出了一种新颖的深度学习方法，名为Dual-CLVSA，以预测具有交易数据的金融市场运动，并通过交易数据和相应的社交情感测量值和相应的社交情感测量值，每个序列通过一个单独的序列序列序列频道。我们通过对SPDR SP 500 Trust ETF的历史交易数据进行了回测，评估了方法的性能。实验结果表明，双CLVSA可以有效地融合两种类型的数据，并验证情感测量不仅是金融市场预测的信息，而且还包含额外的有利可图的功能，以提高我们的预测系统的性能。
It is a challenging task to predict financial markets. The complexity of this task is mainly due to the interaction between financial markets and market participants, who are not able to keep rational all the time, and often affected by emotions such as fear and ecstasy. Based on the state-of-the-art approach particularly for financial market predictions, a hybrid convolutional LSTM Based variational sequence-to-sequence model with attention (CLVSA), we propose a novel deep learning approach, named dual-CLVSA, to predict financial market movement with both trading data and the corresponding social sentiment measurements, each through a separate sequence-to-sequence channel. We evaluate the performance of our approach with backtesting on historical trading data of SPDR SP 500 Trust ETF over eight years. The experiment results show that dual-CLVSA can effectively fuse the two types of data, and verify that sentiment measurements are not only informative for financial market predictions, but they also contain extra profitable features to boost the performance of our predicting system.
