Explainable Text-Driven Neural Network for Stock Prediction

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Title: Explainable Text-Driven Neural Network for Stock Prediction
Authors: Yang, LinyiZhang, ZhengXiong, SuWei, LiruiNg, JamesXu, LinaDong, Ruihai
Permanent link: http://hdl.handle.net/10197/10784
Date: 15-Apr-2019
Online since: 2019-06-11T07:03:28Z
Abstract: It has been shown that financial news leads to the fluctuation of stock prices. However, previous work on news-driven financial market prediction focused only on predicting stock price movement without providing an explanation. In this paper, we propose a dual-layer attention-based neural network to address this issue. In the initial stage, we introduce a knowledge-based method to adaptively extract relevant financial news. Then, we use an input attention to pay more attention to the more influential news and concatenate the day embeddings with the output of the news representation. Finally, we use an output attention mechanism to allocate different weights to different days in terms of their contribution to stock price movement. Thorough empirical studies based upon historical prices of several individual stocks demonstrate the superiority of our proposed method in stock price prediction compared to state-of-the-art methods.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: IEEE
Start page: 441
End page: 445
Copyright (published version): 2018 IEEE
Keywords: Stock predictionAttention mechanismExplainable model
DOI: 10.1109/CCIS.2018.8691233
Other versions: http://ccis2018.csp.escience.cn/dct/page/1
Language: en
Status of Item: Peer reviewed
Is part of: 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)
Conference Details: IEEE CCIS 2018: 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), Nanjing, China, 23-25 November 2018
ISBN: 978-1-5386-6005-8
Appears in Collections:Insight Research Collection

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