scholarly journals Multi Layer Feed Forward Neural Network Knowledge Base to Future Stock Market Prediction

2019 ◽  
Vol 8 (11S) ◽  
pp. 1061-1075 ◽  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Stock Price ◽  
Feed Forward Neural Network ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
The Past ◽  
Knowledge Set ◽  
Neural Network Prediction ◽  
Network Prediction

Stock price prediction is always a most challenging task. Artificial Neural Network prediction clears the stock price prediction challenge by forming the training set. By using the past information as the network input, one can predict the expected output of the network. In order to predict the expected result as the accurate we add multi-layer perceptron to the knowledge set we formed from the past historical data available in the nifty NSE and Sensex BSE. This paper proves that proposing the learning knowledge set using multilayer neural network will predict the accurate closing price of future stock in stock market.

MULTIPLES AND STOCK PRICE, NEW APPROACH FOR RELATIVE VALUATION THROUGH NEURAL NETWORK

2020 ◽  
pp. 1-19
Author(s):  
SAIRA YAMIN ◽  
SAQIB GULZAR
Keyword(s):  
Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Stock Exchange ◽  
Forecasting Model ◽  
Valuation Model ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Relative Valuation ◽  
The Relationship

Artificial Neural Networks (ANNs) has been used as a powerful modeling technique for forecasting. In this study, the relationship between multiples and stock prices has been investigated on the Pakistan Stock Exchange 100 Index by incorporating financial modeling through neural network. The aim is to develop multiple-based valuation model to check whether multiples are viable factor in predicting stock movements. Forecasting model has been developed by using neural network. Prediction accuracy of the developed forecasting model has been evaluated. Findings reveal that neural network outperforms in comparison to linear regression and forecasts stock prices with 98% accuracy.

scholarly journals Prediction of Short-Term Stock Price Trend Based on Multiview RBF Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bailin Lv ◽  
Yizhang Jiang
Keyword(s):  
Neural Network ◽  
Collaborative Learning ◽  
Prediction Model ◽  
Stock Price ◽  
Rbf Neural Network ◽  
Single Type ◽  
Neural Network Modeling ◽  
Learning Capabilities ◽  
Neural Network Prediction ◽  
Network Prediction

Stock price prediction is important in both financial and commercial domains, and using neural networks to forecast stock prices has been a topic of ongoing research and development. Traditional prediction models are often based on a single type of data and do not account for the interplay of many variables. This study covers a radial basis neural network modeling technique with multiview collaborative learning capabilities for incorporating the impacts of numerous elements into the prediction model. This research offers a multiview RBF neural network prediction model based on the classic RBF network by integrating a collaborative learning item with multiview learning capabilities (MV-RBF). MV-RBF can make full use of both the internal information provided by the correlation between each view and the distinct characteristics of each view to form independent sample information. By using two separate stock qualities as input feature information for trials, this study proves the viability of the multiview RBF neural network prediction model on a real data set.

Efficient Secure Neural Network Prediction Protocol Reducing Accuracy Degradation

2020 ◽  
Vol E103.A (12) ◽  
pp. 1367-1380
Author(s):  
Naohisa NISHIDA ◽  
Tatsumi OBA ◽  
Yuji UNAGAMI ◽  
Jason PAUL CRUZ ◽  
Naoto YANAI ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Network Prediction ◽  
Network Prediction
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