An innovative neural network approach for stock market prediction

This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysi...

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Veröffentlicht in:The Journal of supercomputing Jg. 76; H. 3; S. 2098 - 2118
Hauptverfasser: Pang, Xiongwen, Zhou, Yanqiang, Wang, Pan, Lin, Weiwei, Chang, Victor
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.03.2020
Springer Nature B.V
Schlagworte:
Algorithms
Coders
Compilers
Computer Science
Financial analysis
Interpreters
Machine learning
Model accuracy
Multimedia
Neural networks
Pricing
Processor Architectures
Programming Languages
Securities markets
Short term
Stock exchanges
Stock vector
Automatic encoder
Stock market prediction
Long short-term memory neural network (LSTM neural network)
ISSN:0920-8542, 1573-0484
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Zusammenfassung:This paper aims to develop an innovative neural network approach to achieve better stock market predictions. Data were obtained from the live stock market for real-time and off-line analysis and results of visualizations and analytics to demonstrate Internet of Multimedia of Things for stock analysis. To study the influence of market characteristics on stock prices, traditional neural network algorithms may incorrectly predict the stock market, since the initial weight of the random selection problem can be easily prone to incorrect predictions. Based on the development 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 (LSTM) with embedded layer and the long short-term memory neural network with automatic encoder to predict the stock market. In these two models, we use the embedded layer and the automatic encoder, respectively, 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 LSTM with embedded layer is better. Specifically, the accuracy of two models is 57.2 and 56.9%, respectively, for the Shanghai A-shares composite index. Furthermore, they are 52.4 and 52.5%, respectively, for individual stocks. We demonstrate research contributions in IMMT for neural network-based financial analysis.
Bibliographie:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2228-y