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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 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.

Conflicting results have emerged from several past studies as to whether bankruptcy prediction models are able to forecast corporate failure more accurately than auditors’ going‐concern opinions. Nevertheless, the last decade has seen improved modelling of the path‐to‐failure of financially distressed firms over earlier static models of bankruptcy. In the light of the current crisis facing the auditing profession, this study evaluates the efficacy of auditors’ going‐concern opinions in comparison to two bankruptcy prediction models. Bankrupt firms in the U.S. service and trade industry sectors were used to compare model predictions against the auditors’ going‐concern opinion for two years prior to firm failure. The two models are the well‐known Altman (1968) Multiple Discriminant Analysis (MDA) model that includes only financial ratio variables in its formulation and the newer, temporal logit model of Cybinski (2000, 2003) that includes explicit factors of the business cycles in addition to variables internal to the firm. The results show overall better bankruptcy classification rates for the temporal model than for the Altman model or audit opinion.

Recent research questions whether bankruptcy is the best proxy for assessing going concern since filing for bankruptcy is not synonymous with the invalidity of the going concern assumption. Furthermore, in contrast to debtor‐oriented countries such as the USA, liquidation is the most likely outcome of corporate insolvency in creditor‐oriented countries such as the UK, Germany, Australia and New Zealand. This suggests that bankruptcy prediction models have limited use for assessing going concern in creditor‐oriented countries. This study examines the efficacy of a corporate liquidation model and a benchmark bankruptcy prediction model for assessing company liquidation. It finds that the former is more accurate in predicting company liquidations in comparison with the latter. Most importantly, Type 1 errors for the liquidation prediction model are significantly lower than for the bankruptcy prediction model, which indicates its greater efficacy as an analytical tool for assessing going concern. The results also suggest that bankruptcy prediction models might not be appropriate for assessing going concern in countries where the insolvency code is creditor‐oriented.

The study seeks to identify the impact of variables in a deep learning-based bankruptcy prediction model, which has achieved superior performance to other prediction models but cannot easily interpret hidden processes.

This study developed three LSTM-RNN–based models that predicted the probability of bankruptcy before 1, 2 and 3 years using financial, the construction market and macroeconomic variables as input variables. Then, the impacts of the input variables that affected prediction accuracy in each model were identified by using Shapley value and compared among the three models. This study also investigated the prediction accuracy using variants of input variables grouped sequentially by high-impact ranking.

The results showed that the prediction accuracies were largely impacted by “housing starts” in all models. As the prediction period increased, the effects of macroeconomic variables on prediction accuracy increased, whereas the impact of “return on assets” on prediction accuracy decreased. It also found that the “current ratio” and “debt ratio” significantly influenced the prediction accuracies in all models. Also, the results revealed that similar prediction accuracies could be achieved using only 8, 10, and 10 variables out of a total of 18 variables for the 1-, 2-, and 3-year prediction models, respectively.

This study provides a Shapley value-based approach to identify how each input variable in a deep-learning bankruptcy prediction model. The findings of this study can not only assist in obtaining better insights into the underlying concept of bankruptcy but also use to select variables by removing those identified as less significant.

Bankruptcy is the last phase of economic life of companies and has some impacts on all of the entity’s stakeholders. Thus, the prediction of bankruptcy is very important. The inherent aim of preparing and developing financial accounting information is to provide a basis for economic decision-making, and any decision requires information acquisition, processing and data analysis as well as logical and correct interpretation of information. Developing models for predicting financial crisis and comparing the capabilities of existing models can help to alert management about ongoing activities and investors about economic decision for purchase shares or granting loan facilities to companies. So, the purpose of this study is the predict bankruptcy of listed companies on the Tehran Stock Exchange.

From the statistical methods’ perspective, the present research is classified as modeling and with respect to research methodology, it is a correlative-descriptive study in which the relationship between variables is analyzed based on the research objective. Predictive variables are the best ratios of cost of goods sold, non-operating revenues, net sales, predicted earnings per share (EPS) and real EPS.

Prediction of corporate bankruptcy crisis is one of the vital research areas. Predictive models are means for estimating the company’s future situation. Investors and creditors are highly willing to predict the bankruptcy crisis because the high costs associated with bankruptcy crisis will spoil the economy as a whole. On the other hand, this raises concerns among owners, and they are always seeking to find ways to preserve their capital through prediction of stocks continuing operations in the future. Having knowledge about bankruptcy or non-bankruptcy of automotive parts companies makes it possible to recognize weaknesses and strengths in the companies’ current performance and to make investment decisions.

Development of financial markets and, subsequently, creation of fierce competition has resulted in bankruptcy of many companies. Investors are always looking for predicting possible bankruptcy of a firm to prevent their investments risks because bankruptcy costs are high for investors, creditors, lenders and government agencies. Hence, they are seeking ways to estimate corporate bankruptcy. For this reason, over the past four decades, bankruptcy prediction has been enumerated as a key issue in companies and consequently because of its importance, many studies have been conducted to achieve the best model to predict bankruptcy.

Bankruptcy forecast is an economically important issue in every organization and company. Financial and accounting researchers are trying to offer financial models using various combinations of financial ratios with better measuring ability for performance and dividends payments as well as company continued activities. Bankruptcy prediction models are among financial analysis techniques in which the purpose of financial analysis and bankruptcy forecasting is recognition of efficiency and management executive performances. Moreover, the analysis of stock value by shareholder is another application of such research results. Basically, shareholders are interested in knowing the future status of the companies that are going to buy. In this way, shareholders use this method of analysis to estimate future activity or inactivity of firms.

Recent bankruptcy research uses hazard models and extensive samples of companies. The large samples used have precluded the inclusion of a variable related to companies' loan default status in the models. With a sample limited to financially distressed companies, the authors aim to examine if results differ when loan default status and/or audit opinion variables are omitted from hazard bankruptcy prediction models.

The sampling frame is publicly traded US companies, consisting of 111 bankrupt and 310 matching companies from 2003 to 2007. The study applies logistic regression to choose variables for parsimonious bankruptcy prediction models to validate hypotheses. Loan default status and/or audit opinion variables are included as potential predictive variables along with variables included in previous hazard bankruptcy prediction models.

Results reveal that loan default and audit opinion variables: improve the predictive accuracy for financially distressed samples with hazard model characteristics; and change the significance on some variables included in previous hazard models.

Auditors' propensity to issue going‐concern modifications varies over time. To allow manual collection of loan default status information, the authors' sample was limited. Consequently, their results may not be generalizable to other bankruptcy hazard models.

Results from hazard models that do not include loan default status or auditor opinion variables should be interpreted with caution. Auditors might improve their going‐concern modification decisions by attributing more importance to loan default status. Also, the auditor's opinion adds incremental bankruptcy risk information to lenders and investors.

Recent bankruptcy research uses hazard models and extensive samples of companies. However, these studies omit a potentially important variable available to financial statement users, loan default status. The authors demonstrate that including variables for loan default status and auditor's opinion improves bankruptcy prediction models and can change conclusions drawn about other variables.

The purpose of this paper is to investigate whether International Financial Reporting Standards (IFRS)-based data improve bankruptcy prediction over Australian Generally Accepted Accounting Principles (AGAAP)-based data. In doing so, this paper focuses on intangibles because conservative accounting rules for intangibles under IFRS required managers to write off substantial amounts of intangibles previously capitalized and revalued upwards under AGAAP. The focus on intangibles is also motivated by empirical evidence that financially distressed firms are more likely to voluntarily capitalize and make upward revaluations of intangibles compared with healthy firms.

This paper analyses a sample of 46 bankrupt firms and 46 non-bankrupt (healthy) firms using a matched-pair design over the period 1991 to 2004. The authors match control firms on fiscal year, size (total assets), Global Industry Classification Standard-based industry membership and principal activities. Using Altman’s (1968) model, this paper compares the bankruptcy prediction results between bankrupt and non-bankrupt firms for up to five years before bankruptcy. In the tests, the authors use financial statements as reported under AGAAP and two IFRS-based data sets. The IFRS-based datasets are created by considering the adjustments on the AGAAP data required to implement the requirements of IAS 38, IFRS 3 and IAS 36.

This paper finds that, under IFRS, Altman’s (1968) model consistently predicts bankruptcy for bankrupt firms more accurately than under AGAAP for all of the five years prior to bankruptcy. This greater prediction accuracy emanates from smaller values of the inputs to Altman’s model due to conservative accounting rules for intangibles under IFRS. However, this greater accuracy in bankruptcy prediction comes with larger Type II errors for healthy firms. Overall, the results provide evidence that the switch from AGAAP to IFRS improves the quality of information contained in the financial statements for predicting bankruptcy.

Small sample size and having data available over the required period may limit generalizability of findings.

Although bankruptcy prediction is one of the primary uses of accounting information, the burgeoning literature on the benefits of IFRS adoption has so far neglected the role of IFRS data in bankruptcy prediction. Thus, this paper documents a new benefit of IFRS adoption. In this paper, the authors demonstrate how the restrictions on the ability to capitalize and revalue intangibles enhance the quality of information used to predict bankruptcy. These results provide evidence to international standard setters of what they can expect if their efforts to remove non-restrictive accounting practices for intangibles are abandoned.

The purpose of this paper is to improve the information quality of bankruptcy prediction models proposed in the literature by building prediction intervals around the point estimates generated by these models and to determine if the use of the prediction intervals in conjunction with the point estimated yields an improvement in predictive accuracy over traditional models. The authors calculated the point estimates and prediction intervals for a sample of firms from 1991 to 2008. The point estimates and prediction intervals were used in concert to classify firms as bankrupt or non-bankrupt. The accuracy of the tested technique was compared to that of a traditional bankruptcy prediction model. The results indicate that the use of upper and lower bounds in concert with the point estimates yield an improvement in the predictive ability of bankruptcy prediction models. The improvements in overall prediction accuracy and non-bankrupt firm prediction accuracy are statistically significant at the 0.01 level. The authors present a technique that (1) provides a more complete picture of the firm’s status, (2) is derived from multiple forms of evidence, (3) uses a predictive interval technique that is easily repeated, (4) can be generated in a timely manner, (5) can be applied to other bankruptcy prediction models in the literature, and (6) is statistically significantly more accurate than traditional point estimate techniques. The current research is the first known study to use the combination of point estimates and prediction intervals to in bankruptcy prediction.

The purpose of this paper is to explore whether intellectual capital affects the probability that a particular firm will default. The authors also test whether including intellectual capital performance in bankruptcy prediction models improves their predictive ability.

Using a sample of US public companies from the period stretching from 1985 to 2015, the authors test whether intellectual capital performance reduces the probability of bankruptcy. The authors use the VAIC as an aggregate measure of corporate intellectual capital performance.

The findings show that the intellectual capital performance is negatively associated with the probability of default. The findings also indicate that the bankruptcy prediction models that include intellectual capital have a superior predictive ability over the standard models.

This paper contributes to prior research on intellectual capital and firm performance. To the best of the knowledge, this is the first study to show that the benefits of intellectual capital extend from superior performance to long-term financial stability. The research can also contribute to bankruptcy studies. By using a time frame covering decades, the findings suggest that intellectual capital performance measures can be included in bankruptcy prediction models and can effectively complement traditional performance measures.

This paper highlights that intellectual capital is associated with long-term financial stability and a lower bankruptcy risk. Firms realising the potential of their intellectual capital can produce a virtuous circle between higher performance and greater financial stability.