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    政大典藏 > College of Commerce > Department of MIS > Theses >  Item 140.119/111742


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/111742


    Title: 應用機器學習預測利差交易的收益
    Application of machine learning to predicting the returns of carry trade
    Authors: 吳佳真
    Contributors: 蔡瑞煌
    吳佳真
    Keywords: 機器學習
    利差交易
    類神經網路
    TensorFlow
    圖形處理單元
    Machine learning
    Carry trade
    Artificial neural networks (ANN)
    TensorFlow
    Graphic processing unit (GPU)
    Date: 2017
    Issue Date: 2017-08-10 09:46:03 (UTC+8)
    Abstract: 本研究提出了一個類神經網路機制，可以及時有效的預測利差交易(carry trade)的收益。為了實現及時性，我們將通過Tensorflow和圖形處理單元(GPU)來實作這個機制。此外，類神經網路機制需要處理具有概念飄移和異常值的時間序列數據。而我們將透過設計的實驗來驗證這個機制的及時性與有效性。
    在實驗過程中，我們發現在演算法設置不同的參數將影響類神經網路的性能。本研究將討論不同參數下所產生的不同結果。實驗結果表明，我們所提出的類神經網路機制可以預測出利差交易的收益的動向。希望這個研究將對機器學習和金融領域皆有所貢獻。
    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.
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    Description: 碩士
    國立政治大學
    資訊管理學系
    104356020
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0104356020
    Data Type: thesis
    Appears in Collections:[Department of MIS] Theses

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