A Mediated Multi-RNN Hybrid System for Prediction of Stock Prices
Document Type
Conference Proceeding
Publication Date
6-23-2021
Publication Title
Proceedings of the International Conference on Computational Science and Computational Intelligence
DOI
10.1109/CSCI51800.2020.00071
ISBN
978-1-7281-7624-6
Abstract
A multi-recurrent neural network (RNN) hybrid system made up of three RNNs is introduced to predict the stock prices for 10 different companies (five selected from the Dow Jones Industrial Average and five from the Standard and Poor’s 500.) The daily historical data used to train and test the system are collected for the period of October 15, 2013 to March 5, 2019. For each company, the system provides two separate predictions of the daily stock price by using (1) historical stock prices and (2) historical trends along with the historical daily net changes in stock price. The two predictions are mediated to select one as the final output of the hybrid system. For each company, the accuracy of the system was tested for the prediction of the most recent 98 consecutive days using the forecast accuracy measure of the Mean Squared Error (MSR). The results revealed that for every company the difference between the predicted and actual stock price is not statistically different from zero, which is the ideal (error-free) forecast.
Recommended Citation
Hashemi, Ray R., Omid Ardakani, Azita Bahrami, Jeffrey A. Young.
2021.
"A Mediated Multi-RNN Hybrid System for Prediction of Stock Prices."
Proceedings of the International Conference on Computational Science and Computational Intelligence: IEEE.
doi: 10.1109/CSCI51800.2020.00071 source: https://ieeexplore.ieee.org/document/9457849 isbn: 978-1-7281-7624-6
https://digitalcommons.georgiasouthern.edu/economics-facpubs/184
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