JOURNAL OF EMPIRICAL FINANCE，VOL48 ,SEPTEMBER 2018

应用LASSO变量选择技术对市场隐含评级建模

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至关重要的问题。在本文中，我们研究了与金融因素，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，即最小绝对收缩和选择技术（LASSO），我们记录了LASSO选择模型充分的预测能力🪥。另外🥿，当我们将LASSO选择模型与基准利润选择模型进行比较时🙅🏿‍♀️，我们发现前者较于后者在所有样本外预测中具有超强的预测能力。

关键词：市场隐含评级🚶🏻‍➡️🐑，LASSO，财务比率🐒，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/abs/pii/S0927539818300318

翻译：王秭越