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    Title: 機器學習模型進行匯率預測之研究
    Two Essays on Exchange Rate Forecasting with Machine Learning Methods
    Authors: 劉韜
    Liu, Tao
    Contributors: 林建秀
    廖四郎
    Lin, Chien-Hsiu
    Liao, Szu-Lang
    劉韜
    Liu, Tao
    Keywords: 匯率預測
    梯度提升決策樹
    極限梯度提升
    經濟理論因子
    單調性約束
    Exchange rate forecasting
    Gradient Boosting Decision Trees
    eXtreme Gradient Boosting
    Economic fundamental variables
    Monotonic constraint
    Date: 2024
    Issue Date: 2024-07-01 12:32:35 (UTC+8)
    Abstract: 本博士論文由兩篇實證研究組成，探討了機器學習模型在多種貨幣匯率預測和交易性能上的應用。
    第一篇論文利用經濟理論導出的變量，包括利率差、通脹率差、貨幣基要因子和非對稱的泰勒法則因子，運用隨機森林、梯度提升決策樹和極限梯度提升模型進行短期匯率預測和投資組合評估。實證結果表明，這些模型在樣本外預測準確度上均優於隨機漫步和線性回歸模型，尤其是梯度提升決策樹表現最佳。而根據模型的預測結果所構建的外匯投資組合顯示，包含了全部四種因子的梯度提升決策樹模型，可實現最高的累積收益率和夏普比率。此外，非對稱的泰勒法則因子，在所有機器學習模型中對匯率預測的過程中均產生正面的影響，是唯一實現該結果的因子。
    第二篇論文在第一篇的基礎上進行了深化與拓展，主要聚焦於性能更強的極限梯度提升模型，旨在探索進一步提升樣本外匯率預測準確度的方法。一方面，透過豐富解釋變數的組成，在經濟理論因子的基礎上，加入了對匯率變化具有整體性影響的因子、外匯交易策略變數，以及前期的匯率變動值；另一方面，針對部分的解釋變數應用單調性約束，這些約束的設定主要基於匯率決定的經濟理論，並根據本文的實證研究結果進行進一步的優化調整。實證研究結果表明，引入更多元的預測因子，並應用單調性約束，確實顯著提高模型的預測準確度。此外，透過將模型預測的結果運用於外匯投資組合進行交易，其交易表現整體而言優於外匯市場中常見的交易策略；我們還發現，加入的非經濟理論因子對所構建的外匯投資組合所產生的正面影響可能超過經濟理論因子。
    This Ph.D. dissertation comprises two essays exploring the use of machine learning models in forecasting various currency exchange rates and evaluating their trading performance.
    The first essay employs variables derived from economic theories, including the interest rate differential, inflation rate differential, monetary fundamental factor, and asymmetric Taylor rule factor. It utilizes random forests, gradient boosting decision trees (GBDT), and extreme gradient boosting (XGBoost) models for short-term exchange rate forecasting and portfolio evaluation. The results suggest that these models surpass random walk and linear regression models in out-of-sample forecast, with the GBDT model showing superior performance. Furthermore, a foreign exchange portfolio constructed based on the model's predictions, integrating all four factors with the GBDT model, achieves the highest cumulative returns and Sharpe ratio. Notably, the asymmetric Taylor rule factor positively influences the forecasting process across all machine learning models, being the only factor to accomplish such a result.
    Building upon the first essay, the second essay delves deeper and expands upon its foundation, primarily focusing on the more potent XGBoost model, aiming to further enhance the accuracy of out-of-sample exchange rate forecasts. It enriches the explanatory variables by adding factors with systematic impacts on exchange rate changes, foreign exchange trading strategy variables, and the prior exchange rate movements, in addition to economic fundamental factors. Moreover, monotonic constraints are applied to some explanatory variables, grounded in economic theories of exchange rate determination and further optimized based on this essay's empirical findings. The results demonstrate that a broader set of predictive factors and the application of monotonic constraints significantly enhance the model's forecasting accuracy. Additionally, by utilizing the model's predictions for trading in a foreign exchange portfolio, its overall trading performance exceeds that of common trading strategies in the market. The inclusion of non-fundamental variables also appears to positively impact the constructed foreign exchange portfolio, surpassing the influence of fundamental variables.
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    Description: 博士
    國立政治大學
    金融學系
    107352506
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107352506
    Data Type: thesis
    Appears in Collections:[Department of Money and Banking] Theses

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