Binary Response Models for Heterogeneous Panel Data with Interactive Fixed Effects

TL;DR

This paper develops a new binary response model for large heterogeneous panel data with interactive fixed effects, linking maximum likelihood and least squares methods, and demonstrates its application in financial prediction tasks.

Contribution

It introduces a novel framework for binary panel data with interactive fixed effects, including a new information criterion and asymptotic theory, with practical applications in finance.

Findings

Effective prediction of corporate failure and credit ratings.

Successful application to stock return sign prediction and portfolio analysis.

Simulation results confirm theoretical properties.

Abstract

In this paper, we investigate binary response models for heterogeneous panel data with interactive fixed effects by allowing both the cross-sectional dimension and the temporal dimension to diverge. From a practical point of view, the proposed framework can be applied to predict the probability of corporate failure, conduct credit rating analysis, etc. Theoretically and methodologically, we establish a link between a maximum likelihood estimation and a least squares approach, provide a simple information criterion to detect the number of factors, and achieve the asymptotic distributions accordingly. In addition, we conduct intensive simulations to examine the theoretical findings. In the empirical study, we focus on the sign prediction of stock returns, and then use the results of sign forecast to conduct portfolio analysis.