Data mining tools become important in finance and accounting. Their classification and prediction abilities enable them to be used for the purposes of bankruptcy prediction, going concern status and financial distress prediction, management fraud detection, credit risk estimation, and corporate performance prediction. This study aims to provide a state-of-the-art review of the relative literatu...

One of the issues helping make investment decisions is appropriate tools and models to evaluate financial situation 0f the organization.  By means of these tools, investors can analyze financial situation of the organization and identify financial distress or an ideal condition, they become aware of making decisions to invest in appropriate conditions.  The main objective of this study is to ev...

It is suggested in this paper generating SOM as one that could be applied for forecasting of bankruptcy classes for other than trained companies. The various aspects of the proposed Neuro-discriminate model based on the multiple discriminate analysis, supervised learning neural network and self-organizing maps are analyzed. There is compared how accuracy of prediction changes executing algorith...

Bankruptcy prediction have been widely studied as a binary classification problem using financial ratios methodologies. When calculating the ratios, it is common to confront missing data problem. Thus, this paper proposes a classification method Ensemble Nearest Neighbors (ENN) to solve it. ENN uses different nearest neighbors as ensemble classifiers, then make a linear combination of them. Ins...

This study builds on option-pricing theory to explain business bankruptcy based on a sample of 139 matched pairs of bankrupt and control U.S. firms for the period 1983-94. Our results indicate that the primary option-motivated variables, such as firm volatility, play an important role in predicting default, one, two and three years prior to bankruptcy. When the model is extended to account for ...

This paper investigates the forecasting accuracy of bankruptcy hazard rate models for U.S. companies over the time period 1962–1999 using both yearly and monthly observation intervals. The contribution of this paper is multiple-fold. One, using an expanded bankruptcy database we validate the superior forecasting performance of Shumway’s (2001) model as opposed to Altman (1968) and Zmijewski (19...

We investigate the relative importance of various bankruptcy predictors commonly used in the existing literature by applying a variable selection technique, the least absolute shrinkage and selection operator (LASSO), to a comprehensive bankruptcy database. Over the 1980 to 2009 period, LASSO admits the majority of Campbell, Hilscher, and Szilagyi’s (2008) predictive variables into the bankrupt...