High Dimensional Deep Learning of Real-Time Stock Price Forecasting Model by Hybrid Dimension Reduction Method
| Autor: | HSIEH, YI-LIN, 謝易霖 |
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| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 Nowadays in Taiwan people find themselves hard to pay living expenses just by their salaries, and stocks became a popular choice to gain wealth. Stock Price varies with many unpredictable messages or some unperceivable complicated relations, so there are many variables to consider about. If there are ways good enough to reduce dimensions and get features that really changes stock price, it will be able to determine trends and get more remuneration. So, this research uses real time information of Taiwanese stock market, western and some Asian index along with information of Institutional investors related with Margin Trading and Short Selling in Taiwan Stock Exchange, and use autoencoder to reduce dimension and predict the stock prices of three targets: MediaTek, Getac and CTBC Financial Holding. However, the dimension reduces by autoencoder didn't take the effects by response variable into consideration. So we submitted a resolution by adding random forest and change point detection to select further outcomes made by autoencoder. Our results proved that compared to using all variables or use only autoencoder, using autoencoder to decrease dimensions and use random forest and change point detection can get lower RMSE. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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