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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| Vydáno v: | The Journal of supercomputing Ročník 76; číslo 3; s. 2098 - 2118 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
Springer US
01.03.2020
Springer Nature B.V |
| Témata: | |
| ISSN: | 0920-8542, 1573-0484 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Lin, Weiwei Zhou, Yanqiang Wang, Pan Pang, Xiongwen Chang, Victor |
| Author_xml | – sequence: 1 givenname: Xiongwen surname: Pang fullname: Pang, Xiongwen organization: School of Computer, South China Normal University – sequence: 2 givenname: Yanqiang surname: Zhou fullname: Zhou, Yanqiang organization: School of Computer, South China Normal University – sequence: 3 givenname: Pan surname: Wang fullname: Wang, Pan organization: School of Computer, South China Normal University – sequence: 4 givenname: Weiwei surname: Lin fullname: Lin, Weiwei email: linww@scut.edu.cn organization: School of Computer Science and Engineering, South China University of Technology – sequence: 5 givenname: Victor surname: Chang fullname: Chang, Victor organization: International Business School Suzhou, Xi’an Jiaotong-Liverpool University |
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| Copyright | Springer Science+Business Media, LLC, part of Springer Nature 2018 2018© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Keywords | Stock vector Automatic encoder Stock market prediction Long short-term memory neural network (LSTM neural network) |
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| Title | An innovative neural network approach for stock market prediction |
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