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

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

Rashedur M. Rahman, Ruppa K. Thulasiram, Parimala Thulasiraman
Copyright: © 2013 |Pages: 25
ISBN13: 9781466620650|ISBN10: 146662065X|EISBN13: 9781466620667
DOI: 10.4018/978-1-4666-2065-0.ch007
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MLA

Rahman, Rashedur M., et al. "Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting." Applications and Developments in Grid, Cloud, and High Performance Computing, edited by Emmanuel Udoh, IGI Global, 2013, pp. 97-121. https://doi.org/10.4018/978-1-4666-2065-0.ch007

APA

Rahman, R. M., Thulasiram, R. K., & Thulasiraman, P. (2013). Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting. In E. Udoh (Ed.), Applications and Developments in Grid, Cloud, and High Performance Computing (pp. 97-121). IGI Global. https://doi.org/10.4018/978-1-4666-2065-0.ch007

Chicago

Rahman, Rashedur M., Ruppa K. Thulasiram, and Parimala Thulasiraman. "Performance Analysis of Sequential and Parallel Neural Network Algorithm for Stock Price Forecasting." In Applications and Developments in Grid, Cloud, and High Performance Computing, edited by Emmanuel Udoh, 97-121. Hershey, PA: IGI Global, 2013. https://doi.org/10.4018/978-1-4666-2065-0.ch007

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Abstract

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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