固定匯率制度的瓦解，象徵政府操作匯率的干預力道減弱，經貿市場走向全面自由化，台灣也乘著這股浪潮，在國際貿易中扮演不可或缺的角色。然而，頻繁的經貿活動意味著投資買賣風險增加，而匯率變動體現的是一國貨幣購買力出現變化，若無法掌握匯率的升貶，做出錯誤的投資買賣決策，將造成國家企業的巨大損失。因此，預測匯率是許多財經專家與研究人員所關注的議題。
過往針對匯率的研究中，多是探討匯率與各項潛在因子的互動關係，或是運用基於經濟學理論所提出的線性迴歸模型預測匯率。然而，上述兩種研究方法多著墨於總體經濟變數與技術指標的探討，較少利用新聞文本作為預測匯率之依據，因此，本研究參考情緒詞典BosonNLP，考量新聞文本內的情緒詞彙與情緒組合詞以進行情緒分析，藉此表達其對於未來匯率漲跌之信心程度。此外，本研究也彙整過往相關研究所探討的匯率影響因子並進行變數篩選，找出與匯率漲跌最直接相關的變數。最後，採用可以很好地處理時間序列的長短期記憶遞歸神經網路(Long Shot Term Memory Recurrent Neural Network, LSTM)進行匯率預測。實驗結果發現，基於經濟學理論的隨機漫步模型(Random Walk, RW)與廣義自迴歸條件異方差模型(Generalized Autoregressive Conditional Heteroskedasticity, GARCH)的均方根誤差(Root Mean Square Error, RMSE)與預測漲跌準確率(Direction Accuracy, DA)分別為0.119及46.44%與0.083及50.76%，而長短期記憶遞歸神經網路則分別為0.079與64.21%，而在加入新聞文本輔以預測後，RMSE及DA分別進步至0.077與68.27%，尤其在預測匯率變動的方向準確率上得到4.07%的明顯提升。

With the collapse of fixed exchange rate system, the power of the government intervention to control the exchange rate diminished and the financial market has become a free market. Taiwan also plays an important role in the liberalization of international trade. Since frequent trading activities and the investment risk usually come hand in hand, the government and the enterprises will suffer severe capital loss if they are not able to forecast exchange rate so as to make a bad investment decision. Therefore, exchange rate forecasting has become an important research issue for financial experts and researchers.
Most of the previous researches either explored the cause-effect relation between exchange rate and potential factors or forecast exchange rate using linear regression model. Not many researches today have explored enough the effect of textual news on exchange rate. In this thesis, we explore the effect of news articles on exchange rate based on sentiment analysis. To measure the effect of news articles on exchange rate, we extracted sentiment-based words and sentiment-based compound words to calculate the sentimental strength of news articles based on the sentiment lexicon. Besides, to construct an exchange rate forecasting model, we select prediction variables which are the most relevant to the exchange rate forecasting. With the sentiment-based words and the selected variables, we constructed a model to predict the exchange rate fluctuations using the long short-term memory recurrent neural network model. The experiment result showed that the RMSE and the direction accuracy of the random walk model are 0.119 and 46.44%, respectively, while those of the GARCH model are 0.083 and 50.76%, respectively. For the LSTM model, the RMSE and the directional accuracy are 0.079 and 64.21%, respectively. In comparison, the proposed LSTM model which considered the news articles as an additional factor showed a significant improvement on directional accuracy over the pure LSTM model by 4.07%.

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