本論文研究演算法交易(algorithmic trading)對外匯市場價格效率性的影響，採用歐元兌美元及日圓兌美元的日內交易報價資料，建構結構性向量自我迴歸(SVAR)模型進行分析，發現演算法交易與市場交易規模呈現相反的趨勢線圖，且演算法交易與市場價格效率性呈現反向關係，即演算法交易傾向在市場效率性較差時進入市場，最後，發現當演算法交易愈活絡時，市場效率性會隨之提升，說明演算法交易能夠改善市場效率，且可進一步推測演算法交易者為資訊交易者(informed traders)。;This thesis studies the impact of algorithmic trading (AT) on informational efficiency in the foreign exchange market. My data rely on a novel of intraday data consisting of both quote data and transaction data in two currency pairs: euro-dollar, and dollar-yen. The thesis estimates a structural vector autoregression model. The results show that AT exhibits a strong reverse pattern with trade size, and that greater AT activity is related to lower market efficiency which suggests that algorithmic traders strategically enter the market when informational efficiency is lower. AT is associated with an increase in market efficiency in the subsequent intraday period. The results strongly suggest that algorithmic trading is helpful for market efficiency and algorithmic traders are informed.