英文标题：
《Machine Learning for Yield Curve Feature Extraction: Application to
Illiquid Corporate Bonds》
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作者：
Greg Kirczenow, Masoud Hashemi, Ali Fathi and Matt Davison
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最新提交年份：
2018
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英文摘要：
This paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. We consider an example of a hypothetical illiquid fixed income market. After choosing a surrogate liquid market, we apply the Denoising Autoencoder (DAE) algorithm to learn the features of the missing yield parameters from the historical data of the instruments traded in the chosen liquid market. The DAE algorithm is then challenged by two \"point-in-time\" inpainting algorithms taken from the image processing and computer vision domain. It is observed that, when tested on unobserved rate surfaces, the DAE algorithm exhibits superior performance thanks to the features it has learned from the historical shapes of yield curves.
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中文摘要：
本文研究了机器学习在从历史市场隐含的公司债券收益率中提取特征方面的应用。我们考虑一个假设的非流动固定收益市场的例子。在选择替代流动市场后，我们应用去噪自动编码器（DAE）算法从所选流动市场交易工具的历史数据中学习缺失收益率参数的特征。然后，DAE算法受到来自图像处理和计算机视觉领域的两种“时间点”修复算法的挑战。据观察，当在未观测到的速率曲面上进行测试时，DAE算法表现出优异的性能，这得益于它从屈服曲线的历史形状中学到的特性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Statistics 统计学
二级分类：Machine Learning 机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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