High-dimensional censored MIDAS logistic regression for corporate survival forecasting

TL;DR

This paper introduces a novel high-dimensional censored MIDAS logistic regression model for corporate distress forecasting, effectively handling right censoring, mixed-frequency data, and high-dimensional predictors, with theoretical guarantees and practical implementation.

Contribution

It develops a new penalized MIDAS logistic regression method with inverse probability weighting and sparse-group penalties, including inference tools and finite-sample bounds for censored high-dimensional data.

Findings

Demonstrates superior performance through simulations

Successfully applied to predict Chinese firms' financial distress

Provides an R package for practical implementation

Abstract

This paper addresses the challenge of forecasting corporate distress, a problem marked by three key statistical hurdles: (i) right censoring, (ii) high-dimensional predictors, and (iii) mixed-frequency data. To overcome these complexities, we introduce a novel high-dimensional censored MIDAS (Mixed Data Sampling) logistic regression. Our approach handles censoring through inverse probability weighting and achieves accurate estimation with numerous mixed-frequency predictors by employing a sparse-group penalty. We establish finite-sample bounds for the estimation error, accounting for censoring, MIDAS approximation error, and heavy tails. For statistical inference, we develop a de-sparsified version of the proposed penalized estimator and establish its asymptotic theory, which enables valid statistical inference in high-dimensional settings with censoring. We show that censoring induces a nonstandard variance structure for the de-sparsified estimator, a feature that, to the best of our knowledge, has not been studied in the existing literature. The superior performance of the method is demonstrated through Monte Carlo simulations. Finally, we present an extensive application of our methodology to predict the financial distress of Chinese-listed firms and to identify covariates that are statistically significant for predicting distress. Our novel procedure is implemented in the R package \texttt{Survivalml}.