Bankruptcy Prediction with Incomplete Accounting Information

35 Pages Posted: 22 Nov 2022

See all articles by Christian Hilpert

Christian Hilpert

Sun Yat-sen University (SYSU) - Lingnan College

Stefan Hirth

Aarhus University; Danish Finance Institute

Alexander Szimayer

University of Hamburg - Faculty of Economics and Business Administration

Date Written: November 18, 2022

Abstract

How does a creditor’s learning from a firm’s strategic actions affect bankruptcy prediction, debt values, and optimal capital structure? We investigate a Leland (1994) setting augmented by asymmetric information on the firm’s asset value. Observing the firm’s survival of apparently distressed periods, the creditor excludes asset value estimates that are too low to be consistent with the observed survival. We show that the expected bankruptcy threshold decreases as result of the learning. While expected asset and debt values decrease upon reaching new all-time-low asset values, they are persistently higher once the observed asset value recovers to a given level, but the creditor remembers the all-time low. In terms of selecting the capital structure, high quality firms can separate and signal their quality by over-leveraging if the information asymmetry is high enough. Moderate information asymmetry implies a pooling equilibrium.

Keywords: Learning Dynamics, Strategic Interaction, Quantitative Debt Models, Signaling Game

Suggested Citation

Hilpert, Christian and Hirth, Stefan and Szimayer, Alexander, Bankruptcy Prediction with Incomplete Accounting Information (November 18, 2022). Available at SSRN: https://ssrn.com/abstract=4280446 or http://dx.doi.org/10.2139/ssrn.4280446

Christian Hilpert

Sun Yat-sen University (SYSU) - Lingnan College ( email )

GuangZhou, GuangDong
China

Stefan Hirth (Contact Author)

Aarhus University ( email )

Fuglesangs Allé 4
Aarhus V, 8210
Denmark

HOME PAGE: http://hirth.dk

Danish Finance Institute ( email )

Alexander Szimayer

University of Hamburg - Faculty of Economics and Business Administration ( email )

Von-Melle-Park 5
Hamburg, 20146
Germany

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