本研究旨在應用隨機森林演算(Random Forest)計算十年期美國公債期限貼水。隨機森林演算法可以處理非線性數據、多特徵資料，不用特徵篩選，對數據集的適應能力強，近年來在機器學習領域受到廣泛的關注。而美國公債被認為是最安全可靠的金融資產之一，不僅是許多金融商品定價基準，更是各國央行去化龐大外匯準備金的重要金融工具，因此殖利率走勢的預期極為重要，其中美國FED也以ACM估計法透過期限結構模型得出不同天期債券的無風險利率(risk neutral yield)，再將公債殖利率減去中性利率，即可算出不同天期債券的期限貼水。本研究則運用上述資料與Python機器學習套件Scikit-learn的隨機森林迴歸演算法，預測美國十年期公債期限貼水，發現隨機森林迴歸模型預測殖利率準確率高，因此希望透過此監督式機器學習所建立的迴歸模型，作為輔助預測美國公債殖利率的工具。

This study aims to apply the random forest method to forecast the 10-year US treasury yield rate and the term premium. The random forest algorithm has strong adaptability to the data, as it can process the nonlinear and multi-feature data without filtering the variables. As a result, it has been highly eye-catching in the field of machine learning in recent years.U.S. Treasury bond is considered to be one of the safest and the most reliable financial asset. It is not only the pricing benchmark for many financial products, but also an important financial tool for the central banks to reduce large foreign exchange reserves. Therefore, it is vital to forecast the movement of yield rates. The FED uses the ACM method to derive the risk neutral yield from the yield curve traded in market through the term structure model, and gets the the term premium by deducting the neutral interest rate from the bond yield rate. This study applies above data and Scikit-learn's random forest regression algorithm machine learning suite in Python to predict the US 10-year term premium, and the accuracy of the regression prediction yield is high. I hope that the regression prediction model established from supervised machine learning could be used as a tool to assist the investors to forecast the yield rate.

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