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A New Stock Price Forecasting Method Using Active Deep Learning Approach
| Content Provider | MDPI |
|---|---|
| Author | Alkhatib, Khalid Khazaleh, Huthaifa Alkhazaleh, Hamzah Ali Alsoud, Anas Ratib Abualigah, Laith |
| Copyright Year | 2022 |
| Description | Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance. |
| Starting Page | 96 |
| e-ISSN | 21998531 |
| DOI | 10.3390/joitmc8020096 |
| Journal | Journal of Open Innovation: Technology, Market, and Complexity |
| Issue Number | 2 |
| Volume Number | 8 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-27 |
| Access Restriction | Open |
| Subject Keyword | Journal of Open Innovation: Technology, Market, and Complexity Information and Library Science Stock Price Prediction Deep Learning Lstm Cnn Bi-lstm Gru Mlp |
| Content Type | Text |
| Resource Type | Article |
