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| Title: | 機器學習匯率訂價投資組合
Machine Learning for Foreign Exchange Pricing Investment Portfolio |
| Authors: | 林庭陞
Lin, Ting-Sheng |
| Contributors: | 林建秀
林庭陞
Lin, Ting-Sheng |
| Keywords: | 外匯交易
利差交易策略
動能交易策略
價值交易策略
機器學習策略
超參數
FX trade
Carry trade
Momentum strategy
Value strategy
Machine learning strategy
Hyper-parameter |
| Date: | 2020 |
| Issue Date: | 2020-08-03 17:38:46 (UTC+8) |
| Abstract: | 本研究主要是以總經因子(x_t)以及外匯個別因子(c_(i,t))經由寇雷克乘積運算得到的預測因子(z_(i,t))為基礎，進行機器學習模型的訓練，其中包括隨機森林(Random Forest, RF)、梯度提升樹(Gradient Boosted Trees, GBRT)、神經網路(Neural Network, NN)模型。接著，再從驗證集選擇超參數使得外匯超額報酬的預測準確度最高，即驗證集R^2。但實際要追求的是測試集外匯超額報酬的準確度，即測試集R^2。故在訓練期間(1997/01至2015/12)，將共19國貨幣外匯超額報酬，即應變數，及預測因子(z_(i,t))，即自變數做參數估計。旨在探索機器學習模型的測試集R^2與二因子模型(市場因子及利差策略因子)及四因子模型(市場因子、利差、動能及價值策略因子)的高低。最終發現機器學習模型的測試集R^2皆較二因子及四因子模型高。
接著，使用已經訓練好的機器學習模型對測試集的19國貨幣做外匯超額報酬預測，預測為最高的前25%的國家貨幣進行買入，同時預測為最低的後25%的國家貨幣進行賣出。目的就是要對價值、動能及利差策略測度所構建出的買入前25%的國家貨幣，賣出後25%的國家貨幣策略做比較。同時，進行平均值(Avg)、標準差(Std)、夏普比率(Sharpe Ratio)及最大虧損(Max DD)的比較。可發現大抵上機器學習模型的夏普比率較價值、動能及利差策略來的佳。而在驗證集中選擇的超參數可能對R^2帶來的影響也可能是一大重點。整體而言，機器學習模型策略的累積報酬優於價值、動能及利差策略。
This paper mainly trained machine learning models including Random Forest, Gradient Boosted Trees, and Neural Network models based on prediction factors(z_(i,t)) calculated by Kronecker product of macro-economical factors(x_t) and separated foreign exchange factors(c_(i,t)).Then selected the hyper-parameters from the validation set to make prediction accuracy of foreign exchange excess return, namely R^2 of validation set, highest. In reality, we pursued the highest foreign exchange excess return R^2 of test set, namely R^2 of test set. So we used foreign exchange excess return of 19 kinds of currencies(dependent variable) and prediction factors(independent variables) during train set period from January 1997 to December 2015 to estimate parameters of different models. We want to see whether R^2 of test set of machine learning models is higher than that of two-factors and four-factors models. Finally, machine learning models performed better than two-factors and four-factors models indeed.
Next used the well-trained machine learning models to predict foreign exchange excess return of 19 kinds of currencies in the test set. We buy the currencies predicted the top quarter by the models, selling the currencies predicted the bottom quarter. The purpose is to compare with the strategies constructed by different measures of Value, Momentum, and Carry strategies. Meanwhile, we do the comparison of Avg, Std, Sharpe Ratio, and Max DD. Generally, we can find Sharpe Ratio of machine learning models is better than that of Value, Momentum, and Carry strategies. And the hyper-parameters chosen from validation set might be another key point. To sum up, the cumulative return of machine learning models is better than that of Value, Momentum, and Carry strategies. |
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| Description: | 碩士
國立政治大學
金融學系
107352026 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0107352026 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202000764 |
| Appears in Collections: | [金融學系] 學位論文
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