Application of machine learning to predicting the returns of carry trade
碩士 === 國立政治大學 === 資訊管理學系 === 105 === This research derives an artificial neural networks (ANN) mechanism for timely and effectively predicting the return of carry trade. To achieve the timeliness, the ANN mechanism is implemented via the infrastructure of TensorFlow and graphic processing unit (GPU)...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Online Access: | http://ndltd.ncl.edu.tw/handle/8m5pu2 |
| Summary: | 碩士 === 國立政治大學 === 資訊管理學系 === 105 === This research derives an artificial neural networks (ANN) mechanism for timely and effectively predicting the return of carry trade. To achieve the timeliness, the ANN mechanism is implemented via the infrastructure of TensorFlow and graphic processing unit (GPU). Furthermore, the ANN mechanism needs to cope with the time series data that may have concept-drifting phenomenon and outliers. An experiment is also designed to verify the timeliness and effectiveness of the proposed mechanism.
During the experiment, we find that different parameters we set in the algorithm will affect the performance of the neural network. And this research will discuss the different results in different parameters. Our experiment result represents that the proposed ANN mechanism can predict movement of the returns of carry trade well. Hope this research would contribute for both machine learning and finance field.
|
|---|