Dual-CLVSA: a Novel Deep Learning Approach to Predict Financial Markets with Sentiment Measurements

Autor:	Wang, Jia, Zhu, Hongwei, Shen, Jiancheng, Cao, Yu, Liu, Benyuan
Rok vydání:	2022
Předmět:	Quantitative Finance - Statistical Finance Computer Science - Machine Learning Quantitative Finance - Computational Finance Quantitative Finance - Trading and Market Microstructure
Druh dokumentu:	Working Paper
DOI:	10.1109/ICMLA52953.2021.00062
Popis:	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. Comment: 8 pages, 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.03158 Zobrazit plný text záznamu View this record from Arxiv