This study utilizes a sample of private equity backed acquisitions to test whether certain factors, evaluated and quantified on the date of transaction completion, serve as indicators of future transaction bankruptcy. The results of this paper suggest that the effective federal funds rate is significantly and positively correlated with the bankruptcy of private equity backed transactions. Other measured factors specific to the private equity sponsor, the target firm in the acquisition and the characteristics of the transaction are found to be insignificant. Analysis on the influence of these factors is performed using two types of binary-response models, which predict the likelihood of the occurrence of bankruptcy, and a matched sample model that tests for the difference of means between a non-bankrupt transaction group and a bankrupt transaction group. Limitations in the availability of data derived from the private nature of the industry resulted in a limited sample size of 259 transactions completed from 1989 to 2008. General insignificance in the results of this study merits further analysis on the contributing factors to private equity transaction failure.

A financial failure prediction model should be dynamic by adding latest information in an effort to improve the current predictive power, and this model also can be applied to different industries and periods. That is, it has prominent goodness of fit and stable parameter. In this study, I testify that if the modified independent variables, industry relative ratios, can improve the prediction rate by using logistic regression. My research is based on public information. This study constructs two kinds of model¡GModel I is constructed with original financial ratios and Model II with relative industry ratios. Both models incorporate additional variables related to corporate governance. My empirical results suggest that relative industry ratios enhance the predictive power of financial failure prediction within three partially overlapping periods. Further study focus on Model II, I isolated firms which are confronted with financial difficulties and they can¡¦t be discriminated from other normal firms by using the prediction model. My result demonstrates that the main difference between the former and the latter is debt/equity ratio. Those firms which can¡¦t be detected afford less liability. In addition, my studies also compare these undetected firms with their control group and find they still can be distinguished from their control group by using logit model. The accuracy rate of prediction can reach 92.42%. Last study we use event study to research the links between the default possibilities of firms and their stock prices. My results demonstrate that the default possibilities may cause abnormal returns.

This study aims to develop business failure prediction models using the data of selected firms from ISE markets. The sample data comprise ten selected financial ratios for 27 non-going concerns (failed businesses) and paired 27 going concerns. Two non-parametric classification methods are used in the study: Artificial Neural Networks (ANN) and Decision Trees. The classification results show that there is equilibrium in the classification of the training samples by the models, but ANN model outperform the decision tree model in the classification of the testing samples. Further, the potential usefulness of ANN and Decision Tree type data mining techniques in the analysis of complex and non-linear relationships are observed.

Depending on the role of software in everyday life, the cost of a software failure can sometimes be unaffordable. During system execution, errors may occur in system components and failures may be manifested due to these errors. These errors differ with respect to their effects on system behavior and consequent failure manifestation manners. Predicting failures before their manifestation is important to assure system resilience. It helps avoid the cost of failures and enables systems to perform corrective actions prior to failure occurrences. However, effective runtime error detection and failure prediction techniques encounter a prohibitive challenge with respect to the control flow representation of large software systems with intricate control flow structures. In this thesis, we provide a technique for failure prediction from runtime errors of large software systems. Aiming to avoid the possible difficulties and inaccuracies of the existing Control Flow Graph (CFG) structures, we first propose a Connection Dependence Graph (CDG) for control flow representation of large software systems. We describe the CDG structure and explain how to derive it from program source code. Second, we utilize the proposed CDG to provide a connection-based signature approach for control flow error detection. We describe the monitor structure and present the error checking algorithm. Finally, we utilize the detected errors and erroneous state parameters to predict failure occurrences and modes during system runtime. We craft runtime signatures based on these errors and state parameters. Using system error and failure history, we determine a predictive function (an estimator) for each failure mode based on these signatures. Our experimental evaluation for these techniques uses a large open-source software (PostgreSQL 8.4.4 database system). The results show highly efficient control flow representation, error detection, and failure prediction techniques. This work contributes to software reliability by providing a simple and accurate control flow representation and utilizing it to detect runtime errors and predict failure occurrences and modes with high accuracy. / Thesis (Ph.D, Computing) -- Queen's University, 2012-09-25 23:44:12.356

yes / Failure in a cloud system is defined as an even that occurs when the delivered service deviates from the correct intended behavior. As the cloud computing systems continue to grow in scale and complexity, there is an urgent need for cloud service providers (CSP) to guarantee a reliable on-demand resource to their customers in the presence of faults thereby fulfilling their service level agreement (SLA). Component failures in cloud systems are very familiar phenomena. However, large cloud service providers’ data centers should be designed to provide a certain level of availability to the business system. Infrastructure-as-a-service (Iaas) cloud delivery model presents computational resources (CPU and memory), storage resources and networking capacity that ensures high availability in the presence of such failures. The data in-production-faults recorded within a 2 years period has been studied and analyzed from the National Energy Research Scientific computing center (NERSC). Using the real-time data collected from the Computer Failure Data Repository (CFDR), this paper presents the performance of two machine learning (ML) algorithms, Linear Regression (LR) Model and Support Vector Machine (SVM) with a Linear Gaussian kernel for predicting hardware failures in a real-time cloud environment to improve system availability. The performance of the two algorithms have been rigorously evaluated using K-folds cross-validation technique. Furthermore, steps and procedure for future studies has been presented. This research will aid computer hardware companies and cloud service providers (CSP) in designing a reliable fault-tolerant system by providing a better device selection, thereby improving system availability and minimizing unscheduled system downtime.

yes / Failure Prediction has long known to be a challenging problem. With the evolving trend of technology and growing complexity of high-performance cloud data centre infrastructure, focusing on failure becomes very vital particularly when designing systems for the next generation. The traditional runtime fault-tolerance (FT) techniques such as data replication and periodic check-pointing are not very effective to handle the current state of the art emerging computing systems. This has necessitated the urgent need for a robust system with an in-depth understanding of system and component failures as well as the ability to predict accurate potential future system failures. In this paper, we studied data in-production-faults recorded within a five years period from the National Energy Research Scientific computing centre (NERSC). Using the data collected from the Computer Failure Data Repository (CFDR), we developed an effective failure prediction model focusing on high-performance cloud data centre infrastructure. Using the Auto-Regressive Moving Average (ARMA), our model was able to predict potential future failures in the system. Our results also show a failure prediction accuracy of 95%, which is good.

Bakgrund Av bolag som omfattas av frivillig revision väljer 75 procent bort revision trots att den genomsnittliga kostnaden endast uppgår till 10 000 SEK per år för mindre bolag. Syftet med avskaffandet av revisionsplikten år 2010 var att minska småbolagens kostnader för att på så sätt underlätta verksamhetsdriften. Trots att forskning visar att revisorn hjälper till att förbättra och utveckla bolaget väljer majoriteten av bolagen bort den externa kompetensen om möjlighet finns. Resursberoendeteorin säger samtidigt att revisorn är en värdefull resurs som är nödvändig för småbolags överlevnad. Syfte Denna uppsats syftar till att förklara sambandet mellan revision och risken för konkurs för småbolag. Metod Denna kvantitativa studie utgår från en deduktiv ansats. Hypoteser har formulerats med utgångspunkt i resursberoendeteori. En tvärsnittsdesign används med syfte att undersöka risken för konkurs vid en viss tidpunkt. Det empiriska underlaget utgörs av sekundärdata. Slutsats Det finns ett negativt samband mellan revision och risken för konkurs bland svenska småbolag. Revisorn minskar risken för konkurs med 10,42 % varför revision bör ses som en nödvändig resurs för småbolagens överlevnad. / Introduction Despite the fact that the average cost of auditing for SMEs only amounts to 10 000 SEK per year, 75 percent of the SMEs refrain from voluntary audit. The purpose of the abolition of mandatory audit in 2010 was to reduce costs for SMEs in order to benefit their operations. Although research has shown that the auditor helps to improve and develop the company, the majority of the SMEs in Sweden refrains from this external resource. At the same time, according to resource dependence theory, the auditor is a valuable resource that is essential for SMEs. Purpose This study seeks to explain the relationship between auditing and the risk of bankruptcy for SMEs. Method This quantitative study is based on a deductive approach, where hypotheses have been formulated on the basis of resource dependence theory. A cross-sectional design is used in order to study the risk of bankruptcy at a given time. The empirical data consists of archival data. Conclusion There is a negative relationship between auditing and the risk of bankruptcy among SMEs in Sweden. The auditor reduces the risk of bankruptcy with 10.42 %. Hence, the auditor should be seen as essential for the survival of SMEs.

The fiber reinforced polymer (FRP) bars have been widely used in pre-stressing applications and reinforcing of the civil structures. High strength-to-weight ratio and high resistance to the corrosion make the FRP bars a good replacement for steel reinforcing bars in civil engineering applications. According to the CAN/CSA-S806-12 standard, the maximum recommended stress in FRP bars under service loads should not exceed 25% and 65% of the ultimate strength for glass FRP (GFRP) and carbon FRP (CFRP), respectively. These stress values are set to prevent creep failure in FRP bars. However, for in-service applications, there are few physical indicators that these values have been reached or exceeded. In this work analysis of acoustic emission (AE) signals is used. Two new techniques based on pattern recognition and frequency entropy of the isolated acoustic emission (AE) signal are presented for monitoring damage progression and prediction of failure in FRPs. / May 2017

Diante da importância do sistema financeiro para a economia de um país, faz-se necessária sua constante fiscalização. Nesse sentido, a identificação de problemas existentes no cenário bancário apresenta-se fundamental, visto que as crises bancárias ocorridas mundialmente ao longo da história mostraram que a falta de credibilidade bancária e a instabilidade do sistema financeiro geram enormes custos financeiros e sociais. Os modelos de previsão de insolvência bancária são capazes de identificar a condição financeira de um banco devido ao valor correspondente da sua probabilidade de insolvência. Dessa forma, o presente trabalho teve como objetivo identificar os principais indicadores característicos da insolvência de bancos privados no Brasil. Para isso, foi utilizada a técnica de análise de sobrevivência em uma amostra de 70 bancos privados no Brasil, sendo 33 bancos insolventes e 37 bancos solventes. Foi possível identificar os principais indicadores financeiros que apresentaram-se significativos para explicar a insolvência de bancos privados no Brasil e analisar a relação existente entre estes indicador e esta probabilidade. O resultado deste trabalho permitiu a realização de importantes constatações para explicar o fenômeno da insolvência de bancos privados no Brasil, bem como, permitiu constatar alguns aspectos característicos de bancos em momentos anteriores à sua insolvência. / The financial system is very important to the economy of a country, than its supervision is necessary. Accordingly, the identification of problems in the banking scenario is fundamental, since the banking crisis occurring worldwide throughout history have shown that and instability of the financial system generates huge financial and social costs. The banking failure prediction models are able to identify the financial condition of a bank based on the value of its probability of insolvency. Thus, this study aimed to identify the main financial ratios that can explain the insolvency of private banks in Brazil. For this, it was used the survival analysis to analize a sample of 70 private banks in Brazil, with 33 solvent banks and 37 insolvent banks. It was possible to identify the key financial indicators that were significantly to explain the bankruptcy of private banks in Brazil and it was possible to examine the relationship between these financial ratios and the probability of bank failure. The result of this work has enabled the achievement of important findings to explain the phenomenon of the bankruptcy of private banks in Brazil, and has seen some characteristic of banks in times prior to its insolvency.

Diante da importância do sistema financeiro para a economia de um país, faz-se necessária sua constante fiscalização. Nesse sentido, a identificação de problemas existentes no cenário bancário apresenta-se fundamental, visto que as crises bancárias ocorridas mundialmente ao longo da história mostraram que a falta de credibilidade bancária e a instabilidade do sistema financeiro geram enormes custos financeiros e sociais. Os modelos de previsão de insolvência bancária são capazes de identificar a condição financeira de um banco devido ao valor correspondente da sua probabilidade de insolvência. Dessa forma, o presente trabalho teve como objetivo identificar os principais indicadores característicos da insolvência de bancos privados no Brasil. Para isso, foi utilizada a técnica de análise de sobrevivência em uma amostra de 70 bancos privados no Brasil, sendo 33 bancos insolventes e 37 bancos solventes. Foi possível identificar os principais indicadores financeiros que apresentaram-se significativos para explicar a insolvência de bancos privados no Brasil e analisar a relação existente entre estes indicador e esta probabilidade. O resultado deste trabalho permitiu a realização de importantes constatações para explicar o fenômeno da insolvência de bancos privados no Brasil, bem como, permitiu constatar alguns aspectos característicos de bancos em momentos anteriores à sua insolvência. / The financial system is very important to the economy of a country, than its supervision is necessary. Accordingly, the identification of problems in the banking scenario is fundamental, since the banking crisis occurring worldwide throughout history have shown that and instability of the financial system generates huge financial and social costs. The banking failure prediction models are able to identify the financial condition of a bank based on the value of its probability of insolvency. Thus, this study aimed to identify the main financial ratios that can explain the insolvency of private banks in Brazil. For this, it was used the survival analysis to analize a sample of 70 private banks in Brazil, with 33 solvent banks and 37 insolvent banks. It was possible to identify the key financial indicators that were significantly to explain the bankruptcy of private banks in Brazil and it was possible to examine the relationship between these financial ratios and the probability of bank failure. The result of this work has enabled the achievement of important findings to explain the phenomenon of the bankruptcy of private banks in Brazil, and has seen some characteristic of banks in times prior to its insolvency.