Empirical bankruptcy prediction models have been proposed and widely used in the last decades or so. Historic solvent and default firm data are collected and labeled appropriately. Statistical and neural network models are then “trained” to fit these data. A major problem is the imbalance of data, i.e. much more solvent data than default data. We propose a auto-associative neural network(AANN) ...

Bankruptcy prediction has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learn...

judgment of bankruptcy leads to bankruptcy of merchant. therefore, merchant will be incapable and cannot possess in his asset. various provisions can be found in commercial code, regarding contract’s of bankrupt merchant, which is inconsistent with civil code. article 423 has stipulated that trades of merchant after bankruptcy, in three cases will be void. this position is not consistent with c...

We investigate a proposed reform of U.S. personal bankruptcy law which combines Chapters 7 and 13. The proposed reform obliges debtors in bankruptcy to use part of both their wealth and their future earnings to repay debt and therefore bases the obligation to repay in bankruptcy on debtors' ability-to-pay. An important function of personal bankruptcy is to provide partial wealth insurance for r...

This paper introduces an explicit institution of bankruptcy into a general equilibrium model of endogenous ̄nancial markets with asymmetric information. The institution of bankruptcy is modelled as a set of rules that determine if an agent is eligible to have his debts discharged. Those rules are called a bankruptcy code. Given a bankruptcy code, we allow free entry of intermediaries o®ering al...

the financial crisis have caused many writers to become concerned with the effectiveness of existing bankruptcy laws, which encompasses both the usefulness of reorganization and liquidation laws, as well as efficiency of the judicial system to uphold the laws in court. this concern accompanied in our legal system with the privatization movement has caused many banks owned by the government to b...

Existing quantitative-theoretic models of bankruptcy do not make a distinction between bankruptcy and default. In reality, default occurs first and is generally – but not always (and perhaps not even typically) – followed by a bankruptcy filing months later. While we possess reasonably sophisticated models of household default, we do not possess a comparably sophisticated model about the timing...

This paper presents a methodology which aims at detecting mislabeled samples, with a practical example in the field of bankruptcy prediction. Mislabeled samples are found in many classification problems and can bias the training of the desired classifier. This paper proposes a new method based on Extreme Learning Machine (ELM) which allows for identification of the most probable mislabeled samp...

the main purpose of this paper is prediction of tse corporate financial bankruptcy using artificial neural networks. the mean values of key ratios reported in past bankruptcy studies were selected for neural network inputs (working capital to total assets, net income to total assets, total debt to total assets, current assets to current liabilities, quick assets to current liabilities). the neu...

This paper discusses four bankruptcy-related policy issues. First, what is the economic rationale for having a bankruptcy procedure at all and what defines an economically efficient bankruptcy procedure? Second, why did the number of U.S. bankruptcy filings increase so dramatically between 1980 and 2005? Third, a major bankruptcy reform went into effect in the U.S. in 2005—what did it do and ho...