机器学习应用于主动固定收益投资组合管理:Lasso logit方法。

Mercedes de Luis, Emilio Rodríguez, Diego Torres
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引用次数: 4

摘要

定量方法的使用构成了机构投资者投资组合管理工具包的标准组成部分。在过去的十年中,一些实证研究已经使用概率或分类模型来预测股票市场的超额收益,模型债券评级和违约概率,以及预测收益率曲线。就笔者所知,将其应用于主动固定收益管理的研究很少。本文通过比较机器学习算法Lasso logit回归与被动(买入并持有)投资策略,在构建高等级债券投资组合的持续时间管理模型中填补了这一空白,特别是专注于美国国债。此外,提出了一个两步过程,以及一个简单的集成平均,旨在最大限度地减少传统机器学习算法的潜在过拟合。本文还介绍了一种选择阈值的方法,将概率转化为基于条件概率分布的信号。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning applied to active fixed-income portfolio management: a Lasso logit approach.
The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
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