臺灣博碩士論文加值系統

外匯市場為最活躍的金融市場，每日的成交量非常龐大。外匯的波動深深地影響我們的生活，不論是對個人，或是對企業家，甚至影響了國家做經濟上的決策，因此，外匯的預測相當重要。希望能透過機器學習的技術，捕捉外匯市場的特性，做到更貼近的市場趨勢預估。
此篇研究選用的機器學習模型為隨機森林、xgboost演算法與長短期記憶模型，並藉此深入探討不同特徵對模型匯率趨勢預測的影響，如技術指標與總體經濟因子，與探究有無加入總體經濟因子對模型的影響程度。研究中，將探討如何利用機器學習的模型，來預測不同天數的外匯匯率市場趨勢，並採用相對應的交易策略，達到期望的投資績效。

The foreign exchange market is the most active financial market, and the daily trading volume is very large. The volatility of foreign exchange deeply affects our lives, whether it is for individuals, entrepreneurs, or even the government to make decisions. Therefore, forecasting foreign exchange is very important. We want to use machine learning technologies to capture the characteristics of the foreign exchange market and make predictions of the market trends.
The machine learning models used in this study are random forests, xgboost algorithm, and long short-term memory model, and use the model to explore the impact of different features on the model, such as technical indicators and economic factors, and explore the influence of economic factors on the model's prediction. In the study, we will explore how to use machine learning models to predict the market trend of foreign exchange rates on different days, and use corresponding trading strategies to achieve desired investment performance.

目 錄
口試委員審定書 i
致謝 ii
中文摘要 iii
英文摘要 iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 諸論 1
第一節 研究動機 1
第二節 研究目的 2
第三節 研究流程與論文架構 2
第二章 文獻探討 4
第一節 外匯預測 4
第二節 技術指標 5
第三節 總體經濟因子 7
第三章 研究方法 12
第一節 研究對象與資料來源 12
第二節 資料前處理與特徵工程 13
第三節 外匯匯率趨勢標籤設定 28
第四節 模型預測 29
第五節 投資策略與績效評估 32
第四章 研究結果 33
第一節 模型預測結果 33
第二節 交易策略與回溯測試結果 42
第三節 模型預測結果之特徵探討 46
第五章 結論與未來方向 53
第一節 結論 53
第二節 未來方向 54
參考文獻 55

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