A hybrid swarm optimization for neural network training with application in stock price forecasting

2016 ◽  
Author(s):  
Jianjia Pan ◽  
Yuan Yan Tang ◽  
Yulong Wang ◽  
Xianwei Zheng ◽  
Huiwu Luo ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Price Forecasting ◽  
Neural Network Training ◽  
Hybrid Swarm ◽  
Swarm Optimization ◽  
Stock Price Forecasting ◽  
Network Training

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.

Sign in / Sign up

Export Citation Format

Share Document