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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/129212


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    題名: 應用機器學習於促進利差交易收益之預測
    Machine learning for improving the predictability
 of returns on carry trade
    作者: 林亞璇
    Lin, Ya-Hsuan
    貢獻者: 蔡瑞煌
    盧敬植
    Tsaih, Rua-Huan
    Lu, Ching-Chih
    林亞璇
    Lin, Ya-Hsuan
    關鍵詞: 利差交易
    機器學習
    類神經網路
    Carry Trade
    Artificial Neural Networks
    Machine Learning
    GPU
    TensorFlow
    日期: 2018
    上傳時間: 2020-04-06 14:43:51 (UTC+8)
    摘要: 過去二十年來，全球化使得國際資金流量變得越來越方便因為其帶來了巨量的國際貿易。隨之而來的是海外金融市場增加了大量的投資。外匯交易市場擁有幾乎全天都能進行交易的特性，投資者能利用這種便利性投注資金在不同貨幣的利率差上，因此在許多不同的投資工具中，貨幣利差交易（carry trade）越來越受歡迎。近年來它已經成為一項利潤豐厚的業務，有一些國家為了刺激經濟而將利率降至接近於零，而其他國家仍然維持高利率來應對通貨膨脹，這造就了利率差的產生。在經濟相關的文獻中，大多數貨幣利差交易研究仍然使用線性回歸模型來研究貨幣利差交易的時間序列可預測性，而沒有識別資料集中可能存在的複雜非線性關係。本研究嘗試通過實作更複雜的人工神經網絡（ANN）模型來改變這種情況。我們將透過文獻探討確認預測利差交易收益的因子，然後採用能夠有效進行資料清理和機器學習的ANN機制來預測利差交易的回報。由於資料具有時間序列的特徵，我們增加移動視窗（moving window）在機制裡以利學習和忘記來增加預測資料的有效性。為了加速學習，我們還使用TensorFlow和GPU來實現ANN機制。
    In the past two decades, globalization makes it easy for international cash flows because of huge volume of international trades. Along came the vast amount of foreign investments in the financial markets. Among those many different investment vehicles, currency carry trades became popular because foreign exchange markets now work around the clock and investors have used that convenience to take advantage of the difference between interest rates in different currencies. It has become a lucrative business in recent years because some countries cut their interest rates close to zero to stimulate their economy, while other countries still have high interest rates to combat inflation. In the economic literature of carry trades, most studies still use linear regression models to explore the time-series predictability of currency carry trades without identifying possible complex nonlinear relationships in data sets. This study tries to change that by implementing a more sophisticated Artificial Neural Networks (ANN) model. We connect the economic literature on the factors that predicts the profitability of carry trades and then adopt an ANN mechanism that can effectively conduct data cleaning and machine learning to predict the returns on carry trades. Since data has all time-series features, we add the moving window mechanism to facilitate learning and forgetting to increase the effectiveness of predictability. In order to speed up the learning, we also use TensorFlow and GPU to implement the ANN mechanism.
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    描述: 碩士
    國立政治大學
    資訊管理學系
    105356031
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0105356031
    資料類型: thesis
    DOI: 10.6814/NCCU202000390
    顯示於類別:[資訊管理學系] 學位論文

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