Application of Ensemble Learning with Principal Component Analysis in Stock Indices
| Autor: | Wang, Hao, 王皓 |
|---|---|
| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 Previous studies have been devoted to the predictability of the asset price. Some researchers used technical indicators as independent variables to conduct the ordinary least square, and they get the significant predictability. Recently, the development of the machine learning allows us to conduct regression in higher dimension. This paper conduct three non-linear model: support vector regression, random forest and recurrent neural network. In order to enhance the predictability of the models, we apply ensemble learning to combine the result. Compare to other researches, we not only use the technical indicators, but also consider the features of indices futures and options. In addition, we expand our target indices to 9 kinds of indices and lengthen investigation timeline to 20 years. Besides prediction, we also build a trading strategy, and we find that both recurrent neural network and ensemble learning can beat the market, where ensemble learning methods brings the highest return, and this demonstrates the predictability of the ensemble learning skills. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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