We develop a real options perspective to explore how an entrepreneur-friendly bankruptcy law can encourage entrepreneurship development at the societal level. If bankrupt entrepreneurs are excessively punished for failure, they may let inherently high-risk but potentially high-return opportunities pass. We suggest that a more entrepreneur-friendly bankruptcy law, informed by a real options logi...

This study examines the economic rationale for limiting firms’ risk. We argue that risk increases the cost of doing business for two reasons. First, risk causes operating inefficiencies and imposes adjustment costs. Second, diverse stakeholders must be compensated for their risk-bearing. We find empirical support for positive risk-cost relations using various model specifications and risk measu...

This paper integrates the problem of designing corporate bankruptcy rules into a theory of optimal debt structure. We show that, in an optimal contracting framework with imperfect renegotiation, having multiple creditors increases a firm’s debt capacity while increasing its incentives to default strategically. The optimal debt contract gives creditors claims that are jointly inconsistent in cas...

The problem of bankruptcy forecasting is one of the most actively studied nowadays, posing the task of building effective classifiers as well as the task of dealing with dataset imbalance. In this paper, we apply different combinations of modern learning algorithms (MDA, LR, CRT, and ANNs) in order to try to identify the most effective approach to bankruptcy prediction for Russian manufacturing...

We use a heterogeneous-agent model, in which labor income is risky and markets are incomplete, to analyze consumer debt portfolios of secured and unsecured debt in the US. Compared with previous research, we emphasize the role of durables which not only generate utility but also serve as debt collateral. This allows a meaningful joint analysis of secured and unsecured debt and introduces endoge...

This paper examines the performance of the Morningstar Solvency Score, Morningstar's new accounting-ratio based metric for predicting bankruptcy, in comparison to the Altman Z-Score and Distance to Default models. Specifically we tested the following: 1. The ordinal ability of each model to distinguish companies most likely to file for bankruptcy from those least likely to file for bankruptcy a...

Bankruptcy prediction has been an important decision-making process for nancial analysts. One of the most common approaches for the bankruptcy prediction problem is the Discrim-inant Analysis. Also, the k-Nearest Neighbor classiier is very successful in such domains. This paper proposes a Feature Projection based classiication algorithm, and explores its applicability to the problem of predicti...

This Article explores the relationship between consumer credit markets and bankruptcy policy. In general, I argue that the causative relationships running between borrowing and bankruptcy compel a new strategy for policing the conduct of lenders and borrowers in modern consumer credit markets. The strategy must be sensitive to the role of the credit card in lending markets and must recognize th...

This paper examines North American pulp and paper company bankruptcies that occurred between 1990 and 2009. We demonstrate that shareholders suffer substantial losses (37%) during the month a bankruptcy occurs. Encouragingly, we show that financial ratios are useful in predicting firm failure and that failed firms are less profitable, more liquidity constrained and higher in debt leverage. Usin...

Knowledge discovery in databases (KDD) is the process of discovering interesting knowledge from large amounts of data. However, real-world datasets have problems such as incompleteness, redundancy, inconsistency, noise, etc. All these problems affect the performance of data mining algorithms. Thus, preprocessing techniques are essential in allowing knowledge to be extracted from data. This work...