Journal of Empirical Finance, Volume 48, September 2018, Pages 19-35

采用LASSO变量选择技术建立市场隐含评级模型

作者：Georgios Sermpinis (University of Glasgow)

Serafeim Tsoukas (University of Glasgow)

Ping Zhang (University of Glasgow)

摘要：准确预测企业信用评级对投资者和评级机构来说都是一个至关重要的问题。在本文中🧖🏿‍♂️，我们研究了与财务因素👧🏽、市场驱动指标和宏观经济预测因素相关的市场隐含信用评级的决定因素🚀。应用变量选择技术，最小绝对收缩和选择算子（LASSO），我们的研究表现出了实质的预测能力。此外，当我们将LASSO选择模型与基准顺序概率模型进行比较时，我们发现前者具有出众的预测能力🧸，并且在所有样本外预测中都优于后者👨🏽‍🌾。

关键词：市场隐含评级；LASSO🦹🏽‍♂️；金融比率；预测

Modelling market implied ratings using LASSO variable selection techniques

Georgios Sermpinis (University of Glasgow), Serafeim Tsoukas (University of Glasgow)🎚，Ping Zhang (University of Glasgow)

ABSTRACT

Making accurate predictions of corporate credit ratings is a crucial issue to both investors and rating agencies. In this paper, we investigate the determinants of market implied credit ratings in relation to financial factors, market-driven indicators and macroeconomic predictors. Applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), we document substantial predictive ability. In addition, when we compare our LASSO-selected models with the benchmark ordered probit model, we find that the former models have superior predictive power and outperform the latter model in all out-of-sample predictions.

Keywords: Market implied ratings; LASSO; Financial ratios; Forecasting

原文链接：

https://www.sciencedirect.com/science/article/pii/S0927539818300318

翻译：陈然