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Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.

The primary teaching objective is to discuss the capital raising efforts of a firm under financial distress. It also provides supporting data to calculate cost of capital, DuPont/modified DuPont values and Altman’s Z-Score that can appropriately be incorporated into the discussion. Case-B provides information and data of the company’s recent performance and to changes in bankruptcy law in India. Overall, this case study provides ample scope to discuss, understand and provide the solution to the following key corporate finance themes as follows: 1. Analyzing accounting statements and examine potential earnings quality issue. 2. Predicting default and bankruptcy using qualitative analysis, financial ratios, traditional and modified DuPont models and Altman’s Z score model. 3. Examining the capital raising efforts of a distressed firm, which has already defaulted on borrowings. 4. To explore the impact of changes in regulation on the turnaround efforts of the firm as well as on the promoters of the firm.

Since 2005, Amtek Auto moved at a breathtaking speed with the goal of reaching $10bn in sales, from the current level of about $1.2bn. The group had acquired more than a dozen companies spending about Rs.5,000cr. ($850m) during this period primarily through borrowed funds. However, the market and business expansion was not happening as expected. The company’s capacity utilization was just about 40% (approx.) during much of this period. The mounting fixed costs of operation and debt servicing grew to the level of unsustainability, led the firm to default on its borrowing. Now the company had to quickly recapitalize itself to run its operations and retain the premier position in auto component industry. The company and its promoters were considering various methods of debt restructuring, asset sale and further equity infusion.

Introductory and elective level corporate finance.

Teaching notes are available for educators only.

CSS 1: Accounting and Finance.

Retailer bankruptcy provides an opportunity for studying the relationships among members of a channel of distribution because bankruptcy disturbs the ongoing pattern of such relationships. This study employs qualitative research to model the criteria that suppliers use in selecting their response to a bankrupt retailer. Results show that suppliers who continue cooperative behavior with a retailer employ a model that assesses whether the risks in continuing to supply that retailer are in balance with the outcomes generated from the relationship. However, behavioral variables appear relevant as well: retailers appear to benefit from adopting a collaborative communication strategy and building supplier confidence so that a turnaround will in fact occur.

In an effort to further explain why manufacturing firms that move towards service provision often do not achieve the financial benefits they would expect, the purpose of this paper is to examine the impact of service additions on the risks affecting the firm.

Using data drawn from a sample 129 bankrupt manufacturers (75 servitized and 54 non-servitized) and a categorization framework of failure risks, the study explores the impact of the presence of a service business on environmental and internal bankruptcy risks that a manufacturing firm must face.

The study finds that the presence of a service business leads to a greater number of bankruptcy risks for the supplying firm. This is essentially because of greater internal risks. In addition, two types of service offerings are identified – demand chain and product support services. When firms offer demand chain services, they are also exposed to greater environmental risks.

The study provides empirical evidence on the relationship between servitization and bankruptcy risks, and on how this is influenced by the type of service offering. The research should be extended by a more comprehensive assessment of organizational risks in order to further validate and develop the conclusions.

The study suggests that, as adding services introduces new risks for firms, managers have to seek means of mitigating these risks to ensure successful introduction of services.

The paper addressed the gap in the literature for structured analyses of the risk consequences of service strategies.

A neglected area of research in ODC is the turnaround of poorly performing firms such as those under bankruptcy protection. We researched 142 companies that attempted reorganization under bankruptcy protection between 1983 and 2003. Firms deployed one or more of four distinct strategies to turnaround: rationalizing existing resources, developing existing resources, generating new resources, and investing in future resources. Firms that generated new resources, and developed and rationalized existing resources, had the highest probability of emergence. Interestingly firms that sustained their turnaround post-emergence invested in future resources in addition to generating, developing, and rationalizing resources. Implications for ODC are discussed.

Accounting literature is replete with quantitative models that use financial ratios to identify the probability of a going concern qualification. These studies, however, ignore qualitative cues that auditors use to identify going concern problems and mitigating factors (sound financial plans etc.) that auditors take into account in their choice of report. Tests whether, in the presence of financial distress, non‐financial cues play an important role in auditors’ choice. Results indicate that non‐financial variables can be used to discriminate between the auditor’s decision to issue the going concern qualified versus the clean report. Helps company management understand how auditors evaluate their clients and the importance of the qualitative criteria used in their evaluation. Can be used to predict the most probable outcome prior to the external audit. Second, facilitates understanding of the non‐financial red flags that could trigger the going concern report. Third, can be used to analyze potential acquisition targets, and, if the acquisition target is still otherwise desirable, be used in pricing negotiations. Fourth, can be applied to aspects of the firm’s own division’s operations in order to enable the internal audit department to better allocate its own investigational and problem‐solving resources. Finally, the fact that qualitative factors have power in predicting the going concern modified report suggests that company decision makers can evaluate others even if the auditor for political or other reasons has chosen not to render a modified report.

The Mexican housing industry was hindered by a shrinking market and tighter financial conditions related to the Great Recession. Moreover, in 2013, a major change in public policy further modified this industry’s environment. Mexico’s new urban development policy supported inner-city new housing, in contrast to the previous policy that incentivized sprawling. Three out of eight publicly traded housing companies filed for bankruptcy protection in 2013-2014, arguably because of the effects of the Great Recession and the new housing policy. The purpose of this study is to identify firm-level factors that caused some firms to file for bankruptcy protection.

Three approaches were used to analyze the housing industry in Mexico from 2006 to 2015. First, a policy analysis focused on the new housing policy and its consequences for housebuilding companies. Second, a financial analysis of the two economic shocks was performed in search for the transmission mechanisms in the companies’ financial metrics. Third, a retrospective analysis using the Fisher’s exact test was used to identify variables statistically associated with companies filing for bankruptcy protection.

There are two features significantly associated with bankruptcy protection: increasing market share while being vertically integrated, as a response to the Great Recession, and the relative magnitude of the loss on firms’ inventory value due to the new public policy. Neither Altman’s Z-score values nor firm size or degree of integration are significantly related to bankruptcy.

The small sample size presented a challenge, as most statistical methodologies require large samples; however, this was overcome by using the Fisher’s exact test.

The main contribution of this paper is the statistical identification of the possible causes for bankruptcy protection in Mexico amongst homebuilding firms in 2013 and 2014, which have not previously been reported in the literature.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

The purpose of this paper is to enhance the predictive power of bankruptcy prediction models by taking the past values of firms’ financial ratios as benchmark. For this purpose, the paper proposes an indicator variable expressing the time trends of financial ratios.

The proposed measure uses the minimum and the maximum of financial ratios from the previous period as benchmarks in order to give a more complete picture about the present financial performance of firms. The most popular classification methods of bankruptcy prediction were employed: discriminant analysis, logistic regression, decision trees. Sample specific results and conclusions were avoided by applying tenfold stratified cross-validation.

The empirical results suggest that the proposed measure can increase the predictive performance of bankruptcy prediction models compared to models based solely on static financial ratios. The results gave evidence for the fact that the firms’ past financial performance is a useful benchmark for evaluating the risk of future insolvency.

The proposed concept is completely new to the literature and practice of bankruptcy prediction. Similar concept has not been published to date. The suggested dynamization approach has three important advantages. It is easy to compute from time series of financial ratios. It is applicable within any classifier irrespective of its mathematical background. The performance of models can be enhanced without the necessity of giving up the interpretability of bankruptcy models, so the proposed measure may play very important role in the practice of credit scoring modeling as well.