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| Title: | 應用機器學習於外匯超額報酬預測
Applying Machine Learning for the Prediction of Foreign Exchange Return |
| Authors: | 朱珮錡
Chu, Pei-Chi |
| Contributors: | 林建秀
Lin, Chien-Hsiu
朱珮錡
Chu, Pei-Chi |
| Keywords: | 外匯交易策略
機器學習
隨機森林
梯度提升樹
極限梯度提升樹
Forex strategy
Machine learning
Random Forest
Gradient Boosting Decision Tree
Extreme Gradient Boosting |
| Date: | 2021 |
| Issue Date: | 2021-08-04 14:51:45 (UTC+8) |
| Abstract: | 影響匯率走勢的因素有央行利率決策、一國之經濟數據及當地政府治理、政治因素等，透過分析重要因素預測匯率未來變化，能讓企業使用較少成本做匯率避險、外匯投機者能從交易策略中賺取報酬。本研究將機器學習技術應用於外匯市場上，結合影響匯率走勢的重要因素及機器學習模型，嘗試對匯率變動方向作預測，以十六個國家的外匯資料及六十個因子為研究變數。
本研究藉由隨機森林(Random Forest)、梯度提升樹(GBDT)及極限梯度提升樹(XGBoost)演算法預測匯率走勢，並比較各模型的預測準確度，以及統整出影響匯率走勢之重要因素，也將模型的預測結果建構出投資組合，並使用評量指標分析模型的績效表現。本研究也利用實證上有效的因子，如：利差因子、動能因子、價值因子、市場因子，將此四個因子同時納入機器學習模型及羅吉斯回歸中，比較四因子使用不同方式做預測的差異，也將預測結果建構成投資組合，與實證上有超額報酬的外匯交易策略做比較。
實證結果顯示，機器學習模型在預測匯率變動方向上比羅吉斯回歸佳，顯示機器學習確實有分類預測能力；使用六十個因子的預測準確度優於使用四個因子，顯示較多因子能涵蓋更多資訊，可得到更好預測結果；三種機器學習模型的預測準確度差異不大。在模型之重要因子方面，三種機器學習模型中，政府效能都是相當重要的因子，顯示一國政府治理、政治局勢是否穩定是影響匯率重要因素。在投資組合績效表現上，四因子納入機器學習模型優於實證上有效的單一策略，顯示多因子機器學習模型建構出來的策略比單一因子更好且更穩定；另外，也發現GBDT與XGBoost的投資組合有較高累積報酬，表現優於隨機森林。
Forex rates can be predicted by analyzing factors, like interest rate decisions, economic data, and political situations. If companies can have better opinions about forex rates, they can use less cost for hedging or speculators can earn returns from some strategies. In this study, I used factors in machine learning models to predict forex rates.
In this study, Random Forest, Gradient Boosting Decision Tree and Extreme Gradient Boosting are used to predict forex rates. Forecast accuracy are compared in three models and important factors are found out. Predictions are formed a portfolio and the performance are compared between models. I also use the factors of Carry, Market, Momentum and Value in machine learning models and Logistic Regression to compare predictions and performance of trading strategies between models.
Results show machine learning models are better than logistic regression in predicting the direction of forex changes. Besides, the accuracy of sixty factors is better than four factors. However, the accuracy between three machine learning models is not much different. In the feature importance, I found factors related to governance are important in all machine learning models. In the portfolio performance, using four factors is better than a single strategy, showing that a portfolio constructed by multi-factor machine learning model is better than a single factor. Besides, the study found GBDT and XGBoost are better than Random Forest in performance. |
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| Description: | 碩士
國立政治大學
金融學系
108352023 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108352023 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202100724 |
| Appears in Collections: | [金融學系] 學位論文
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