| Autor: |
Azita Bahrami, Ray R. Hashemi, Jeffrey A. Young, Omid M. Ardakani |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
2020 International Conference on Computational Science and Computational Intelligence (CSCI). |
| DOI: |
10.1109/csci51800.2020.00071 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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