Search results

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.

Research in the area of financial distress often uses a proportional hazards model to determine the influence of covariates on the duration of time that precedes financial distress. Acritical issue in the use of a proportional hazards model is the use of time‐invariant and time‐dependent covariates. Time‐invariant covariates remain fixed while time‐dependent covariates change during the estimation of the model. Although the choice of covariates might substantially affect the estimation of the proportional hazards model, existing literature often fails to consider the potential effect of this choice on model estimation. This paper reviews the distinction between time‐invariant and time‐dependent covariates and the effect of covariate selection on the estimation of a proportional hazards model. Using a sample of financially distressed and non‐financially distressed firms, this paper suggests the choice of time dependence substantially influences model estimation and that covariate selection should be given more serious consideration in financial distress research.

This study aims to focus on asset management of large‐scale information systems supporting infrastructures and especially seeks to address a methodology of their statistical deterioration prediction based on their historical inspection data. Information systems are composed of many devices. Deterioration process i.e. wear‐out failure generation process of those devices is formulated by a Weibull hazard model. Furthermore, in order to consider the heterogeneity of the hazard rate of each device, the random proportional Weibull hazard model, which expresses the heterogeneity of the hazard rate as random variables, is to be proposed.

Large‐scale information systems comprise many components, and different types of components might have different hazard rates. Therefore, when analyzing faults of information systems that comprise various types of devices and components, it is important to consider the heterogeneity of the hazard rates that exist between the different types of components. In this study, with this in consideration, the random proportional Weibull hazard model, whose heterogeneity of hazard rates is subject to a gamma distribution, is formulated and a methodology is proposed which estimates the failure rate of various components comprising an information system.

Through a case study using a traffic control system for expressways, the validity of the proposed model is empirically verified. Concretely, as for HDD, the service life at which the survival probability is 50 percent is estimated as 158 months. However, even for the same HDD, use environment differs according to usage. Actually, among the three different usages (PC, server, others), failures happen earliest in the case of PCs, which have the highest heterogeneity parameter and a survival probability of 50 percent after 135 months of usage. On the other hand, as for others, its survival probability is 50 percent at 303 months.

To operationally express the heterogeneity of failure rates, the Weibull hazard model is employed as a base, and a random proportional Weibull hazard model expressing the proportional heterogeneity of hazard rates with a standard gamma distribution is formulated. By estimating the parameter of the standard proportional Weibull hazard function and the parameter of the probability distribution that expresses the heterogeneity of the proportionality constant between the types, the random proportional Weibull hazard model can easily express the heterogeneity of the hazard rates between types and components.

This paper investigates how well regulator examinations predict bank failures and how best to incorporate examination information into an econometric model of time‐to‐failure. We estimate proportional hazard models with time‐varying covariates and find that examiner ratings help explain the failure hazard. Both the overall rating of a bank's condition and management, i.e., the composite CAMELS rating, and ratings of specific components contain information. In addition, we find that the marginal “effect” of ratings is non‐linear, in that the impact of a rating downgrade on the hazard is larger, the weaker a bank's initial rating.

The software reliability models to describe the reliability growth phenomenon are formulated by any stochastic point process with state‐dependent or time‐dependent intensity function. On the other hand, to deal with the environmental data, which consists of covariates influencing times to software failure, it may be useful to apply the Cox’s proportional hazards model for assessing the software reliability. In this paper, we review the proportional hazards software reliability models and discuss the problem to determine the optimal software release time under the expected total software cost criterion. Numerical examples are devoted to examine the dependence of the covariate structure in both the software reliability prediction and the optimal software release decision.

Reviews previous research on predicting financial distress and the effects of US Chapter 11 bankruptcy (C11B); and explains how survival analysis and Cox’s (1972) proportional hazards model can be used to estimate the financial outcome for the shareholders of C11B. Reduces a previous data set (Russel et al 1999) of 154 companies entering C11B between 1984 and 1993 to 59 (54 of which gave no value to shareholders) and estimates two models to predict this: one based on firm‐specific covariates only and the other adding market‐wide covariates. Explains the methodology, presents the results and uses receiver operating characteristic curves to compare the predictive accuracy of the two. Finds little difference between the and suggests using the simpler model. Briefly summarizes the variables which are most useful in predicting the value outcomes of C11B for shareholders and recognizes the limitations of the study.

Peat swamp forest fire hazard areas were identified and mapped by integrating GIS‐grid‐based and multi‐criteria analysis to provide valuable information about the areas most likely to be affected by fire in the Pekan District, south of Pahang, Malaysia. A spatially weighted index model was implemented to develop the fire hazard assessment model used in this study. Fire‐causing factors such as land use, road network, slope, aspect and elevation data were used in this application. A two‐mosaic Landsat TM scene was used to extract land use parameters of the study area. A triangle irregular network was generated from the digitized topographic map to produce a slope risk map, an aspect risk map and an elevation risk map. Spatial analysis was applied to reclassify and overlay all grid hazard maps to produce a final peat swamp forest fire hazard map. To validate the model, the actual fire occurrence map was compared with the fire hazard zone area derived from the model. The model can be used only for specific areas, and other criteria should be considered if the model is used for other areas. The results show that most of the actual fire spots are located in very high and high fire risk zones identified by the model.

The purpose of this paper is to apply duration methods to a sample of Farm Service Agency (FSA) direct, seven‐year operating loans to identify those variables that influence the time to loan termination and type of termination. Variables include both those known at time of loan origination and those that characterize the changing economic environment over the life of the loan. Also, to examine the impact of various FSA programs promoting policy objectives.

A systematic sample of 877 seven‐year, FSA direct loans originated between October 1, 1993 and September 30, 1996 was collected. Cox regression, competing risks models are estimated as a function of borrower and loan characteristics observable at loan origination. Economic indicator variables emphasizing the farm economy and observed quarterly over the life of the loan are also included as explanatory variables.

Loan characteristics, borrower financial characteristics and degree of borrower interaction with FSA observable at origin are significant variables in determining type of loan outcome (default or paid‐in‐full) and time to outcome. Changes in the economic environment and farm economy during the life of the loan are significant.

The sample consists only of FSA direct loans which implies borrowers are at financial margin. Application of method to agricultural loans from conventional commercial lenders could identify different significant factors.

Using length of time to loan termination instead of just type of outcome provides for a richer analysis of loan performance. Loan performance over time is influenced by the larger economy and should be incorporated into loan performance modeling.

The study described in the paper demonstrates use of competing risks models on intermediate agricultural loans and develops how this technique can be used to learn about dynamic aspects of loan performance. Sample consists of observations on individual FSA direct loan borrowers. The FSA direct loan program is the major source of credit for agricultural borrowers at the financial margin.

This paper aims to present a case study where proportional hazards modeling software is used to evaluate the potential benefits of a condition‐based maintenance policy for military vehicle diesel engines.

Maintenance records for diesel engines were supplied by the UK Ministry of Defence. A proportional hazards model based on these data was created using EXAKT software. Covariate parameters were estimated using the maximum likelihood method and transition probabilities were established using a Markov Chain model. Finally, decision parameters were entered to create an optimal decision model.

Two significant covariates were identified as influencing the hazard rate of the engines. In addition, the optimal decision model indicated a potential economic saving of up to 30 per cent.

A model of this nature is particularly useful to predict failures, improve maintenance policies, and possibly reduce maintenance costs. In addition, the cost of implementing maintenance policies based on this model should be balanced with the potential to reduce the risk of danger to personnel.

The model presented provides military personnel with a decision tool that optimizes the maintenance policy for diesel engines installed in military vehicles.

Building on the steps to war model, this paper seeks to examine the impact that territorial Militarized Interstate Disputes (MID) have on the time it takes a dyad to go to war after it experiences its first MID.

A model common to epidemiological research, the hazard model, is employed to examine the dyadic relationship from the time of the first MID forward. This is an improvement to dyadic analysis, as most research examines the characteristics of individual MIDs in isolation.

Dyads with a history of territorial MIDs go to war much more quickly than dyads without a history of territorial MIDs. Future research should explore the relationship between territory, war, and power status to test the assertion that minor power states engage in power politics behavior less frequently.

Conflict resolution measures need to be employed more quickly when states have unresolved territorial issues. Mediation generally does not occur quickly, which may explain why territorial issues are less likely to be referred to mediators and less successfully mediated. The results presented herein highlight the need for flexible, quick responses to certain crises and the need to settle borders and other territorial disputes permanently to avoid war.

The paper tests a critical component of the steps to war model and examines the assertion that the historical relationship between states affects conflict decisions.