论文标题
机器学习国库产量
Machine Learning Treasury Yields
论文作者
论文摘要
我们提供了使用（无监督的）机器学习（ML）技术（例如非负矩阵分解（NMF）和（统计上确定性的）群集，提供了用于提取库存库存因子的明确算法和源代码。 NMF是一种流行的ML算法（用于计算机视觉，生物信息学/计算生物学，文档分类等），但经常被误解和滥用。我们讨论如何将NMF正确应用于国库产量。我们根据NMF和聚类及其解释分析这些因素。我们讨论了他们在样本外ML稳定性问题的背景下对预测国库产量的影响。
We give explicit algorithms and source code for extracting factors underlying Treasury yields using (unsupervised) machine learning (ML) techniques, such as nonnegative matrix factorization (NMF) and (statistically deterministic) clustering. NMF is a popular ML algorithm (used in computer vision, bioinformatics/computational biology, document classification, etc.), but is often misconstrued and misused. We discuss how to properly apply NMF to Treasury yields. We analyze the factors based on NMF and clustering and their interpretation. We discuss their implications for forecasting Treasury yields in the context of out-of-sample ML stability issues.
