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Two-channel Attention Mechanism Fusion Model of Stock Price Prediction Based on CNN-LSTM

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Published:22 July 2021Publication History
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Abstract

Using hierarchical CNN, the company's multiple news is characterized as three levels: sentence vectors, chapter vectors, and enterprise sentiment vectors. By combining the stock price data with the news lyric data at the same time, the influence of news on price is used to achieve correlation analysis of news information and stock prices. A two-channel attention mechanism fusion model based on CNN-LSTM is proposed. After the dual-channel feature extraction, the attention layer fusion layer is used to convert the weighted values of LSTM hidden variables, so the stock price can be predicted with the news text.

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      • Published in

        cover image ACM Transactions on Asian and Low-Resource Language Information Processing
        ACM Transactions on Asian and Low-Resource Language Information Processing  Volume 20, Issue 5
        September 2021
        320 pages
        ISSN:2375-4699
        EISSN:2375-4702
        DOI:10.1145/3467024
        Issue’s Table of Contents

        Copyright © 2021 Association for Computing Machinery.

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 22 July 2021
        • Accepted: 1 March 2021
        • Revised: 1 December 2020
        • Received: 1 February 2020
        Published in tallip Volume 20, Issue 5

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