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Corporate failure remains a critical financial concern, with implications for both firms and financial institutions; this paper aims to review the literature that proposes corporate failure prediction models for the twenty-first century.

This paper gathers information from 106 published articles that contain corporate failure prediction models. The focus of the analysis is on the elements needed to design corporate failure prediction models (definition of failure, sample approach, prediction methods, variables and evaluation metrics and performance). The in-depth review creates a synthesis of current trends, from the view of those elements.

Both consensus and divergences emerge regarding the design of corporate failure prediction models. On the one hand, authors agree about the use of bankruptcy as a definition of failure and that at least two evaluation metrics are needed to examine model performance for each class, individually and in general. On the other hand, they disagree about data collection procedures. Although several explanatory variables have been considered, all of them serve as complements for the primarily used financial information. Finally, the selection of prediction methods depends entirely on the research objective. These discrepancies suggest fundamental advances in discovery and establish valuable ideas for further research.

This paper reveals some caveats and provides extensive, comprehensible guidelines for corporate failure prediction, which researchers can leverage as they continue to investigate this critical financial subject. It also suggests fruitful directions to develop further experiments.

Machine learning (ML) models are increasingly being used in industrial maintenance to predict system failures. However, less is known about how the time windows for reading data and making predictions affect performance. Therefore, the purpose of this research is to assess the impact of different sliding windows on prediction performance.

The authors conducted a factorial experiment using high dimensional machine data covering two years of operation, taken from a real industrial case for the production of high-precision milled and turned parts. The impacts of different reading and prediction windows were tested for three ML algorithms (random forest, support vector machines and logistic regression) and four metrics (accuracy, precision, recall and F-score).

The results reveal (1) the critical role of the prediction window contingent upon the application domain, (2) a non-monotonic relationship between the reading window and performance, and (3) how sliding window selection can systematically be used to improve different facets of performance.

The study's findings advance the knowledge of ML-based failure prediction, by highlighting how systematic variation of two important but yet understudied factors contributes to the development of more useful prediction models.

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.

This paper aims to review the existing literature systematically so as to contribute towards a better understanding of methodological problems of the classical statistical techniques, artificially intelligent expert systems and theoretical approaches to solve the corporate failure syndrome.

This paper presented a systematic review of 83 articles reporting 137 prediction failure models published within 1966-2012 in scholarly reviewed journals in four main disciplines, namely, accounting, finance, banking and economics. The authors performed the systematic literature review with five main sources, namely, Science Direct, Google Scholar, Wiley Interscience, Metalib, Web of Science and Business Source Complete of the Social Sciences. The review modified the approaches used by Aziz and Dar (2006), Ravi and Ravi (2007) and Balcaen and Ooghe (2006).

The results indicate significant body of prior literature on prediction of corporate failure, but a theoretically sound, highly accurate, simple and widely used corporate failure prediction model for stakeholders has yet to be developed.

This paper contributes towards a systematic understanding of the methodological problems associated with the statistical, artificially intelligent expert systems and theoretical approaches to solve the corporate failure prediction problems faced by firms in 11 countries.

In this study the factors behind the decision‐makers’ erroneous judgements regarding failure prediction (classification of firms as bankrupt and non‐bankrupt) are analysed. The purpose is to find out the factors causing incorrect responses, i.e. the cases in which the decision‐maker is for some reason incapable of using the given information to arrive at the correct classification. The following five possible sources of disturbance in this decision‐making were hypothesized: firm‐specific factors, data, decision‐maker‐specific factors, external factors, and failure process. In further analysis these factors were empirically operationalized and their significance was tested applying logistic (logit) analysis separately for the Type I and Type II classification errors identified in an HIP study. The results indicated that the effect of all of the five hypothesized factors on misclassifications is statistically significant. The inconsistency of the cues (firm‐specific factors) may be the main factor causing errors in evaluation. Moreover, the failure process is another important factor (Type I error). Thus, human bankruptcy prediction can be improved mainly by checking the consistency of financial statements (that they give a true view of the firm’s economic status) and by paying special attention to timely identification of the possible failure process. Future HIP studies on bankruptcy prediction and also other economic events should pay attention to control the kinds of sources of disturbance identified in this study, to maintain validity.

This study aims to obtained the failure probability distributions of subsystems for industrial robot and filtrate its fault data considering the complicated influencing factors of failure rate for industrial robot and numerous epistemic uncertainties.

A fault data screening method and failure rate prediction framework are proposed to investigate industrial robot. First, the failure rate model of the industrial robot with different subsystems is established and then the surrogate model is used to fit bathtub curve of the original industrial robot to obtain the early fault time point. Furthermore, the distribution parameters of the original industrial robot are solved by maximum-likelihood function. Second, the influencing factors of the new industrial robot are quantified, and the epistemic uncertainties are refined using interval analytic hierarchy process method to obtain the correction coefficient of the failure rate.

The failure rate and mean time between failure (MTBF) of predicted new industrial robot are obtained, and the MTBF of predicted new industrial robot is improved compared with that of the original industrial robot.

Failure data of industrial robots is the basis of this prediction method, but it cannot be used for new or similar products, which is the limitation of this method. At the same time, based on the series characteristics of the industrial robot, it is not suitable for parallel or series-parallel systems.

This investigation has important guiding significance to maintenance strategy and spare parts quantity of industrial robot. In addition, this study is of great help to engineers and of great significance to increase the service life and reliability of industrial robots.

This investigation can improve MTBF and extend the service life of industrial robots; furthermore, this method can be applied to predict other mechanical products.

This method can complete the process of fitting, screening and refitting the fault data of the industrial robot, which provides a theoretic basis for reliability growth of the predicted new industrial robot. This investigation has significance to maintenance strategy and spare parts quantity of the industrial robot. Moreover, this method can also be applied to the prediction of other mechanical products.

Online health monitoring of large complex equipment has become a trend in the field of equipment diagnostics and prognostics due to the rapid development of sensing and computing technologies. The purpose of this paper is to construct a more accurate and stable grey model based on similar information fusion to predict the real-time remaining useful life (RUL) of aircraft engines.

First, a referential database is created by applying multiple linear regressions on historical samples. Then similarity matching is conducted between the monitored engine and historical samples. After that, an information fusion grey model is applied to predict the future degradation trajectory of the monitored engine considering the latest trend of monitored sensory data and long-term trends of several similar referential samples, and the real-time RUL is obtained correspondingly.

The results of comparative analysis reveal that the proposed model, which is called similarity-based information fusion grey model (SIFGM), could provide better RUL prediction from the early degradation stage. Furthermore, SIFGM is still able to predict system failures relatively accurately when only partial information of the referential samples is available, making the method a viable choice when the historical whole life cycle data are scarce.

The prediction of SIFGM method is based on a single monotonically changing health indicator (HI) synthesized from monitoring sensory signals, which is assumed to be highly relevant to the degradation processes of the engine.

The SIFGM can be used to predict the degradation trajectories and RULs of those online condition monitoring systems with similar irreversible degradation behaviors before failure occurs, such as aircraft engines and centrifugal pumps.

This paper introduces the similarity information into traditional GM(1,1) model to make it more suitable for long-term RUL prediction and also provide a solution of similarity-based RUL prediction with limited historical whole life cycle data.

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

The purpose of this paper is to focus on the development of a business failure prediction model on a sample of small and medium‐sized firms with head offices located in the region of Castilla y León (Spain), in order to prove the significance of non‐financial information on the prediction of business failure.

In order to reach the authors' aim, one of the most used predictive statistical methods in this field (logistic regression) is applied, in which the authors consider financial ratios and non‐financial information as potential variables to predict failure. But before developing the respective models, in order to reduce the number of variables, a principal components analysis (PCA) is first applied. Then, the achieved results with this analysis are used in the prediction step, so as to estimate the models.

The results of the predictive method show that non‐financial information, which becomes significant in the developed models, helps financial ratios to improve the ability to predict failure, so any business failure model should also consider both types of information to be accurate.

Most of the developed business failure prediction models have used a paired sample with the same number of failed and non‐failed firms, which has the drawback of not being representative of the population from which it is chosen. In order to obtain a representative sample, a random sampling method is applied, on the basis of the population size and composition. The selected sample assures that parameter estimates are not inconsistent and biased, as the statistical methods assume.