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Parallel Implementation of Moving Averages and Stock Market Prediction

John Jenq
Computer Science Department, Montclair State University, Montclair, New Jersey, USA
The 2012 International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA’12), 2012
@article{jenq2012parallel,

   title={Parallel Implementation of Moving Averages and Stock Market Prediction},

   author={Jenq, J.},

   year={2012}

}

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In recent years, graphics processing units have made parallel processing affordable with the price of personal desktop computers. This report investigates the computational aspects of calculating simple moving average and exponential moving average operations, two of the most popular financial indicators. In this report, we also investigate the usage of GPU to run artificial neural network as a mean of predicting stock market pricing. Feedforward and Backpropagation artificial neural network was used for this study. Financial data including major stock indices, volumes, pricing, and moving average of stocks were used as input. The future stock prices can be predicted as the output. The speedup factor by adopting GPU and CPU together over traditional CPU alone implementation was not significant. The computation of compute moving averages on GPU was also discussed.
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