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Authors: Xiongwen Pang 1 ; Yanqiang Zhou 1 ; Pan Wang 2 ; Weiwei Lin 3 and Victor Chang 4

Affiliations: 1 South China Normal University, China ; 2 China Merchants Bank Branch in Wuhan, China ; 3 School of Computer Science and Engineering and South China University of Technology, China ; 4 International Business School Suzhou and Xi’an Jiaotong-Liverpool University, China

ISBN: 978-989-758-297-4

Keyword(s): Embedded Layer, Long-Short Term Memory Neural Network (LSTM Neural Network), Stock Market Prediction, Stock Vector.

Abstract: To study the influence of market characteristics on stock prices, traditional neural network algorithm may also fail to predict the stock market precisely, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the idea of word vector in deep learning, we demonstrate the concept of stock vector. The input is no longer a single index or single stock index, but multi-stock high-dimensional historical data. We propose the deep long-short term memory neural network (LSMN) with embedded layer to predict the stock market. In this model, we use the embedded layer to vectorize the data, in a bid to forecast the stock via long-short term memory neural network. The experimental results show that the deep long short term memory neural network with embedded layer is state-of-the-art in developing countries. Specifically, the accuracy of this model is 57.2% for the Shanghai A-shares composite index. Furthermore, this is 52.4% for individual stocks. (More)

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Paper citation in several formats:
Pang, X.; Zhou, Y.; Wang, P.; Lin, W. and Chang, V. (2018). Stock Market Prediction based on Deep Long Short Term Memory Neural Network.In Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS, ISBN 978-989-758-297-4, pages 102-108. DOI: 10.5220/0006749901020108

@conference{complexis18,
author={Xiongwen Pang. and Yanqiang Zhou. and Pan Wang. and Weiwei Lin. and Victor Chang.},
title={Stock Market Prediction based on Deep Long Short Term Memory Neural Network},
booktitle={Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,},
year={2018},
pages={102-108},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006749901020108},
isbn={978-989-758-297-4},
}

TY - CONF

JO - Proceedings of the 3rd International Conference on Complexity, Future Information Systems and Risk - Volume 1: COMPLEXIS,
TI - Stock Market Prediction based on Deep Long Short Term Memory Neural Network
SN - 978-989-758-297-4
AU - Pang, X.
AU - Zhou, Y.
AU - Wang, P.
AU - Lin, W.
AU - Chang, V.
PY - 2018
SP - 102
EP - 108
DO - 10.5220/0006749901020108

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