本文主要在探討在較為短的時間段以及不同的金融環境之下，是否仍然能捕捉到匯率市場中主要解釋投組報酬變動的共同風險因子－平均超額報酬以及利差報酬。我們依據重要金融事件將全樣本分為八個子樣本；總共使用39種幣別並將1983年11月至2015年10月的遠期貼水由小到大排序後，依序建構六個投資組合。全文以美國投資者的觀點出發。結果顯示平均超額報酬無論是在長期或短期的時間段下，仍然為匯率市場中解釋匯率報酬變動的主要風險因子。然而，利差報酬則不然。在銀行危機期間，利差報酬與第二主要成分之相關係數皆為高度負相關。近期自2008年次貸危機開始，利差報酬與解釋投組變動的第二主要成分之相關係數也從先前的0.8~0.9降至-0.80.此結果顯示利差交易似乎在次貸危機之後有所轉變。此外利差風險因子無法有效的解釋動能報酬。 / This paper investigates whether or not the common risk factors, dollar and carry trade risk, in currency markets proposed by Lustig, Roussanov and Verdelhan (2011) will still exist even under a short-run period with a concern of different financial backgrounds. A split of full sample into eight subsamples with respect of financial events is made. A total of 39 currencies is used to build six portfolios on the basis of the forward discounts from November 1983 to October 2015. The whole paper is in the view of an American investor. The finding suggests that under both long-run and short-run period, the dollar return is always the common factor in currency markets. However, it is not the same case for the carry trade return. During bank crises, the carry trade return is strongly negative correlated with the second component. The carry trade return turns out to have a negative correlation with the second component during and after the subprime crisis, decreasing from 0.8~0.9 in the previous subsamples to -0.80. It indicates that the desirability of carry trade activities has changed since the subprime crisis. Besides, the carry trade risk has a little power to explain the variations of momentum returns.

本研究主要是以「外匯報酬三因子模型」為基礎，故先檢視在本樣本期間內(1985/2至2016/10) ，以雙分類法將37國主流貨幣分為9個投組後，外匯超額報酬解釋力，是否會因加入動能策略因子形成之三因子模型，而較原本兩因子模型(市場因子、利差策略因子)來的強？最終測得三因子模型在判斷係數及殘差等適切度表現較佳。 接著利用逐步迴歸分析法(限制所有自變數均須於90%信心水準內顯著)嘗試尋找獲利成因，主要挑選出不同面向之11種經濟成因因子(股價指數波動、投機活動、流動性、貨幣波動、落後短期利率、落後股利率、落後期限利差、落後違約利差、)落後避險基金套利資本、工業生產量及通膨率因子)來檢測可否解釋三因子模型中獲取報酬之利差、動能策略因子，並利用Fama-MacBeth兩步驟橫斷面迴歸法評估模型市場定價能力。結果發現定價能力均顯著，而利差交易策略之成因為股價指數波動因子(△EVOL)，因其可能連動匯率波動而呈現負相關；動能交易策略成因則為股價指數波動因子(△EVOL)及落後期限利差因子(△LTS)，主要因動能交易主要來自於市場資訊反應不完全，前者成因因子提供更大的動量執行交易策略、後者則因投資人在不同景氣循環下而有不同的投資反應，如景氣擴張的過度自信與樂觀、景氣衰退下產生行為財務領域中的處置效果，使兩成因與動能策略因子呈現正相關。 / This paper is based on the model of three-factors foreign exchange returns. So we test whether three-factors FX model which adds the factor of momentum can have stronger ability to explain currency excess return than two-factors FX model in the sampling period of February 1985 to October 2016. And the 37 kinds of currency are sorted by double sort method and become 9 portfolios. Finally, no matter coefficient of determination or residual error, three-factors FX model performs well. Further, we use stepwise LS regression (independent variable should have statistical significance in 90% confidence interval) to find which factor we choose can cause carry and momentum strategy profit in three-factors FX model. Next, using Fama-MacBeth two-step regression to estimate the asset pricing ability. The results represent that all contribution factors which get from stepwise LS method are significant. Carry trade strategy and △EVOL are negative correlation, because volatility of stock index will influence volatility of FX. And there have the positive correlation between momentum trade strategy and two factors(△EVOL and △LTS). Just because the profit from momentum strategy comes from the incomplete reaction of market information and △EVOL give more motive force. Besides, there have different investment reactions in diverse business cycle. Investors are over confident and optimistic during the period of recession and have disposition effect during the period of boom.

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.