長期以來，由於匯率的波動將造成物價、股市、進出口…等等的變動，匯率預測一直是許多學者關注的議題。本論文嘗試預測短期(一、六和十二個月後)的匯率變動方向，以提供市場擇時操作(market timing)的資訊。本研究假設兩個國家間相對的總體經濟情況變化，將影響匯率的變動。而一個國家的領先指標，能適當地代表該國未來的總體經濟情況。透過類神經網路模型，本研究建構出兩國各自的領先指標和未來的匯率變動方向間關係。以四個國家(日、加、英、德)的貨幣相對美元的匯率進行實證預測，並和過去研究的測試期(1990~1997.7)結果相比較下，發現在六和十二個月後的預測上，正確性不止優於過去研究，也能夠顯著優於隨機漫步模型。
同時本研究導入灰關聯分析，期望找出較關鍵的領先指標，以增加預測的正確性並減少運算的複雜度。在大部分的實驗結果中，利用灰關聯選取的領先指標進行預測，能比未選取前更普遍的顯著於隨機漫步模型。但預測匯率變動方向的正確率，則未必較未選取前的表現好。
最後，本研究也利用相同的模型，對四個國家(日、加、英、台灣)貨幣近期(2000~2004.5)對美元的匯率變動進行預測。多數國家在一月和六月的預測能優於隨機漫步模型(除了加拿大)，十二月後的預測有時反而正確率較低而且不顯著(日本、台灣)。另外，在此測試期中，不能驗證出進行灰關聯分析的選取的作法是較為有利。
根據實證的結果，本研究能比過去研究表現更佳，達到一定程度的正確率，並且顯著於隨機漫步模型，證明本研究的想法和模型有助於補足目前匯率研究不足之處，值得未來加強研究。

For a long time, prediction on exchange rate change has been a research issue, because the change of exchange rate will directly make commodity price, stock market, the import and export, etc. change. This paper attempts to predict exchange rate direction at the short horizon (1-, 6-, and 12-month) to provide the market-timing information. We presume that the relative variations of two countries’ macroeconomic conditions would therefore lead to exchange rate moving, and the leading indicators of a specific country could appropriately represent its future macroeconomic conditions. Neural network model is employed to describe the relationship between the leading indicators of two countries and the direction of their exchange rate in the future. To forecast four countries (Japan, Canada, U.K., Germany ) exchange rate and to compare the results by the test terms (1990~1997.7) of the past research, we find empirical evidence that our model outperforms the random walk model and appears to be more accurate at 6- and 12-month horizons than past work.
Gray relational analysis is also employed in hope to find out some key indicators. We expect the analysis can promote the prediction accuracy and reduce the operation complex. In greatly part of experiment results, using the indicators selecting by gray relational analysis to predict exchange rate direction can perform statically significantly superior to random walk. But the correct rates of forecasting the exchange rate direction not definitely perform better than unanalyzed.
Finally, this research also use the same model to predict four countries (Japan, Canada, U.K., Taiwan) to US dollar exchange rate direction in near term (2000~2000.5). The forecasting results in 1 and 6 month horizon can surpass random walk model in most countries, but sometimes the correct rates are lower and not significantly superior to random walk model in 12 month horizon. Besides, in this testing horizons, we can''t identify the method of the selection of gray relational analysis is more beneficial.
According to the empirical results, our work can perform statically significantly superior to random walk. The other is that the idea and model of this research can improve the insufficiency of current ones on exchange rate.

第一章、緒論 1
1.1 研究背景 1
1.2 研究動機 2
1.3 論文架構 4
第二章、文獻探討 6
2.1 匯率預測 6
2.2 方向性預測 8
2.3 類神經網路 10
第三章、研究方法 13
3.1 研究假設 13
3.2 實驗架構 16
3.2.1 輸入變數---總體經濟領先指標 16
3.2.2 倒傳遞類神經網路 17
3.2.3 類神經網路架構 20
3.3 實驗數據來源和處理 23
3.4灰關聯 25
3.4.1 灰色理論 25
3.4.2 灰關聯處理 27
3.5 統計分析方法 29
3.5.1 方向正確率 (Directional Accuracy Ratio, DAR) 29
3.5.2 卡方獨立性檢定 29
3.5.3 PT檢定 31
第四章、實驗結果 34
4.1 與過去實驗比較 35
4.1.1 類神經網路模懝 35
4.1.1.1 日圓 35
4.1.1.2加拿大幣 39
4.1.1.3德國馬克 42
4.1.1.4 英鎊 45
4.1.1.5 小結 48
4.1.2 灰關聯對參數進行挑選 49
4.1.2.1 日圓 49
4.1.2.2 加拿大幣 53
4.1.2.3 德國馬克 57
4.1.2.4 英鎊 60
4.1.2.5 小結 63
4.2 近期研究 64
4.2.1 日圓 64
4.2.2 加拿大幣 68
4.2.3 英鎊 72
4.2.4 新台幣 76
4.2.5 小結 81
第五章、結論及未來研究方向 82
5.1 結論 82
5.2 未來研究方向 84
5.2.1 其他領先指標的選取 84
5.2.2 匯率的不穩定周期探討和調整 84
5.2.3 類神經網路架構改良 85
5.2.4 類神經網路和其他MODEL比較 86
參考文獻 87
附表一：實驗中所採用的各國領先指標 110

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