政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/139564

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/139564

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政大典藏 > College of Social Science > Department of Economics > Theses > Item 140.119/139564

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/139564

Title:	機器學習對外匯報酬之預測 Forecast Foreign Exchange Returns with Machine Learning
Authors:	蔡伯甯 Tsai, Bo-Ning
Contributors:	林建秀賴廷緯蔡伯甯 Tsai, Bo-Ning
Keywords:	機器學習自編碼器主成份分析隨機森林極限梯度提升樹外匯超額報酬集成方法 Machine Learning Autoencoder PCA Random Forest XGBoost Foreign Exchange Excess Return Ensemble Method
Date:	2022
Issue Date:	2022-04-01 15:06:21 (UTC+8)
Abstract:	本研究首先使用機器學習的模型，比較主成份分析（PCA）與自動編碼器（Autoencoder）兩種方式做降維後的資料擬合之結果，並且以測試集的R^2衡量表現，結果顯示，經過自動編碼器預訓練後的資料能更大幅度的提升模型性能。下一步，使用前面訓練好的模型作為弱學習器，以簡單平均的方式做集成，比較三個弱學習器與集成後的預測表現，再以模型預測結果作為買賣訊號來建構外匯投資組合，同時，加入利差策略與動量策略作為比較基準，觀察投資組合的績效表現，根據實驗結果，集成後的投組明顯優於個別機器學習模型，而機器學習模型又優於傳統策略。 In this study, we compare two different techniques which are principal component analysis (PCA) and autoencoder(AE) for reducing the dimensionality of data prior to modeling, and deploy machine learning models for data fitting to observe their results. Then, we measure performance by R^2 on the test set. The results showed that the data pre-trained by AE can greatly improve the model performance. The next step is to use previously trained models as weak learners to combine them by simple average method and compare its result to weak learners. After that, we adopt the result of model prediction as a trading signal to construct a foreign exchange portfolio. Moreover, we also add traditional strategies which are carry strategy and momentum strategy as the benchmarks to observe the portfolio performance. According to the experimental results, the composite is better than all weak learners, and all weak learners are better than the traditional strategies.
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Description:	碩士國立政治大學經濟學系 108258042
Source URI:	http://thesis.lib.nccu.edu.tw/record/#G0108258042
Data Type:	thesis
DOI:	10.6814/NCCU202200371
Appears in Collections:	[Department of Economics] Theses

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