| Forecasting Stock Market Price Using Deep Neural Networks |
| کد مقاله : 1226-CFIS (R1) |
| نویسندگان: |
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نیما گوزل پور *، محمد تشنه لب دانشگاه خواجه نصیرالدین طوسی |
| چکیده مقاله: |
| Investors in the stock market expect to make their profits, meaning they are looking for a profit by buying a share at low prices and selling it to the highest level, which requires stock price prediction. Significant challenges arise due to the non-linear and chaotic nature of stock markets when predicting prices in time series. In this research, unsupervised methods were used for the reduction of the input dimension and creating indicators. In the next step, the output of the unsupervised model was fed as input into the Jordan Recursive Neural Network(JRNN) to predict the closing price of the upcoming day. To illustrate the functionality and superiority of the proposed algorithm, data from three different and challenging stock symbols of Nasdaq were used. Ultimately, the model was evaluated with mean square error and mean absolute percentage error. |
| کلیدواژه ها: |
| Forecasting, Principal Component Analysis, Auto-encoder, Jordan Recursive Neural Network. |
| وضعیت : مقاله برای ارائه شفاهی پذیرفته شده است |