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...

Article history: Received 13 August 2010 Received in revised form 12 July 2012 Accepted 17 July 2012 Available online 23 July 2012 We use data from the US airline industry to investigate whether firms that are under bankruptcy protection, as well as these firm's product market rivals, change the quality of the products they offer. We measure the quality of the services offered by a carrier usin...

A personal bankruptcy prediction system running on credit card data is proposed. Personal bankruptcy, which usually results in significant losses to creditors, is a rapidly increasing yet little understood phenomenon. The most commonly used methods in personal bankruptcy prediction are credit scoring models. Some data mining models have also been investigated in this domain. Neither the scoring...

Many static neural networks have been studied extensively in financial classification problems. However, dynamic time series predictive classification using neural networks with memory, such as the Gamma Memory neural network (GMNN), may prove more accurate. In this study we compare the predictive accuracy of the GMNN to the Multilayer Perceptron neural network and the statistical approaches of...

Bankruptcy prediction is of great importance in financial statement analysis to minimize the risk of decision strategies. It attempts to separate distress companies from healthy ones according to some financial indicators. Since the real data usually contains irrelevant, redundant and correlated variables, it is necessary to reduce the dimensionality before performing the prediction. In this pa...

When liability is limited by bankruptcy, theory says that firms will take excessive environmental and public health risks. In the long run, this “judgmentproof problem” may increase the share of small producers, even when there are economies of scale. I use quasi-experimental variation in liability exposure to measure the effects of bankruptcy protection on industry structure and environmental ...

We analyze the interaction of firm product quality and pricing decisions with financial distress and bankruptcy in the airline industry. We consider an airline’s choices of quality and price as dynamic decisions that trade off current cash flows for future revenue. We examine how airline mishandled baggage, on-time performance and pricing are related to financial distress and bankruptcy, contro...