預測股價報酬一直是一個很熱門的議題，而預測匯率也是另一個熱門的領域。本研究利用每一個月發布的經濟數據和月頻率的金融資訊，透過技術分析的動能策略之概念，對台美匯率做預測分析。將月頻率的經濟金融數據轉換成不同期數的動能指標，並將這些指標透過逐步回歸的方法抓出對台美匯率有顯著解釋力的變數與該變數的動能期數，透過每個月篩選一次的過程rolling 後回測績效表現。本研究著重在經濟數據與金融資訊本身變數的漲跌「趨勢」是否能對匯率變動有解釋力，並透過動態的逐步迴歸模型對匯率走勢進行預測。
　　回測結果顯示，此策略的基本模型命中率達5 成，透過增加訊號發射的門檻值和變數時間點不同的權重後模型準確率達6 成且發生訊號比例達5 成，本研究最佳化模型結果為觀察櫥窗10 年，觀察櫥窗10 年之年化報酬為4.52%；累積倍數為1.30；發射訊號次數為71 次；命中率為64.79%，這一個研究結果對於持有龐大外匯部位的台灣公司企業與進出口商而言是一個判定匯率走勢有利的參考依據。

Exchange rate forecasting are very popular issues against stock return forecasting. There are some famous theories about the movement of exchange rate, such as PPP(Purchasing Power Parity) and IRP (Interest Rate Parity). However, the traditional economic theories of exchange rate do not perform well in empirical evidence. The empirical evidence is not favorable to IRP and is not favorable to PPP in short run. Although, there is no strong empirical evidence to PPP in short run. The convergence of Exchange rate movements still work in long run with a slow speed. Therefore, this paper tries to use the monthly data such as macroeconomic variables and financial markets variables to predict the trend of exchange rate. This paper transform the macroeconomic variables and financial variables into momentum indicators. This paper tries to use the momentum of the macroeconomic variables and financial variables to explain the movement of exchange rate. In empirical analysis, this paper applies the step-wise regression model with rolling window to predict the trend of exchange rate. The result shows the shooting average is up to 50%, with threshold adjustment and time-weighted average the predictability comes to 60% in average and with signal rate up to 50%.

論文審定書 ii
摘　要 iii
ABSTRACT iv
第一章 緒論 1
第一節 研究背景 1
第二節 研究目的 2
第三節 研究流程 2
第二章 文獻探討 3
第一節 傳統的經濟學理論與匯率的預測 3
第二節 匯率的可預測性 4
第三章 研究方法 5
第一節 轉換動能指標 5
第二節 變數篩選 7
第三節 逐步迴歸模型回測 8
第四章 實證結果與分析 10
第一節 候選變數與資料 10
第二節 全樣本空間下的單變數檢定 13
第三節 入選變數與敘述性統計 18
第四節 逐步迴歸模型與回測績效 22
第五章 結論與建議 43
第一節 結論 43
第二節 後續研究方向建議 44
參考文獻 45
中文部分 45
英文部分 45

中文部分
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