Research on Stock Price Forecasting Based on BP Neural Network

2021 ◽  
pp. 663-673
Author(s):  
Shuwen Jia ◽  
Tingting Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Stock Price Forecasting

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

scholarly journals Short term stock price forecasting with application of neural network

2021 ◽  
Author(s):  
Jiayin Liu
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Price ◽  
Principal Component ◽  
Price Forecasting ◽  
Short Term ◽  
Stock Price Forecasting ◽  
Price Forecast ◽  
Short Term Prediction

With the world’s rapid economic growth and the expansion of stock market, it produced a large amount of valuable data information. That data become an important investors in stock investment analysis subject Thorough analysis the short-term stock price forecast problem and comparing a variety of stock price forecasting method, on the basis of BP neural network (BPNN) [1] and principal component analysis (PCA）[2] and genetic algorithm and the feasibility of short-term prediction of stock price .BP neural network can use the study of historical stock market data, find out the inherent law of development and change of the stock market, so as to realize the future stock price data changes over a period of time.

Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting

2011 ◽  
Vol 3 (1) ◽  
pp. 45-68 ◽  
Author(s):  
Rashedur M. Rahman ◽  
Ruppa K. Thulasiram ◽  
Parimala Thulasiraman
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Insurance Company ◽  
Memory Architecture ◽  
Backpropagation Algorithm ◽  
Network Algorithms ◽  
Training Time ◽  
Stock Price Forecasting ◽  
Neural Network Algorithm

The neural network is popular and used in many areas within the financial field, such as credit authorization screenings, regularities in security price movements, simulations of market behaviour, and so forth. In this research, the authors use a neural network technique for stock price forecasting of Great West Life, an insurance company based in Winnipeg, Canada. The Backpropagation algorithm is a popular algorithm to train a neural network. However, one drawback of traditional Backpropagation algorithm is that it takes a substantial amount of training time. To expedite the training process, the authors design and develop different parallel and multithreaded neural network algorithms. The authors implement parallel neural network algorithms on both shared memory architecture using OpenMP and distributed memory architecture using MPI and analyze the performance of those algorithms. They also compare the results with traditional auto-regression model to establish accuracy.

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