scholarly journals Saudi Arabia Stock Market Prediction Using Neural Network

2018 ◽  
Vol 9 (2) ◽  
pp. 62-70
Author(s):  
Talal Alotaibi ◽  
Amril Nazir ◽  
Roobaea Alroobaea ◽  
Moteb Alotibi ◽  
Fasal Alsubeai ◽  
...  
Keyword(s):  
Neural Network ◽  
Saudi Arabia ◽  
Stock Market ◽  
Stock Market Prediction

Stock market prediction based on neural network

2021 ◽  
Author(s):  
Chang Huang ◽  
Zhihui Hou ◽  
Yanchu Liu ◽  
Yanlin Wu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Market Prediction

scholarly journals Comparing Artificial Neural Network Architectures for Brazilian Stock Market Prediction

2020 ◽  
Vol 7 (4) ◽  
pp. 613-628
Author(s):  
Suellen Teixeira Zavadzki de Pauli ◽  
Mariana Kleina ◽  
Wagner Hugo Bonat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Network Architectures ◽  
Stock Market Prediction ◽  
Artificial Neural ◽  
Neural Network Architectures

scholarly journals Deep Learning-based Integrated Stacked Model for the Stock Market Prediction

2019 ◽  
Vol 9 (1) ◽  
pp. 5167-5174 ◽  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Stock Market ◽  
Network Models ◽  
Time Series Forecasting ◽  
Convolution Neural Network ◽  
Neural Network Models ◽  
Stock Market Prediction ◽  
Market Data

Recently, the stock market prediction has become one of the essential application areas of time-series forecasting research. The successful prediction of the stock market can be better guided to the investors to maximize their profit and to minimize the risk of investment. The stock market data are very much complex, non-linear and dynamic. Due to this reason, still, it is a challenging task. In recent time, deep learning method has become one of the most popular machine learning methods for time-series forecasting due to their temporal feature extraction capabilities. In this paper, we have proposed a novel Deep Learning-based Integrated Stacked Model (DISM) that integrates both the 1D Convolution neural network and LSTM recurrent neural network to find the spatial and temporal features from the stock market data. Our proposed DISM is applied to forecast the stock market. Here, we have also compared our proposed DISM with the single structured stacked LSTM, and 1D Convolution neural network models, and some other statistical models. We have observed that our proposed DISM produces better results in terms of accuracy and stability.

An innovative neural network approach for stock market prediction

2018 ◽  
Vol 76 (3) ◽  
pp. 2098-2118 ◽  
Author(s):  
Xiongwen Pang ◽  
Yanqiang Zhou ◽  
Pan Wang ◽  
Weiwei Lin ◽  
Victor Chang
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Network Approach ◽  
Neural Network Approach ◽  
Stock Market Prediction
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