Bank Failure prediction: corporate governance and financial indicators

Most failure prediction studies have relied on using financial ratios as predictors. The most suitable financial predictors for banks are financial ratios following the CAMEL rating system. Also, corporate governance has been proven to be an important aspect of banks, especially after the financial crisis. Given its importance, we test the ability of corporate governance to enhance the prediction of bank failure. While there are only few studies that examine efficiency of corporate governance as a failure predictor, there are scarcely any studies that examine it as predictor of US banks failure. Using discriminant analysis, we predict the failure of banks insured by the Federal Deposit Insurance Corporation during the period from 2010 to 2018 using financial and non-financial predictors. We find that combining CAMEL ratios with corporate governance variables not only enhances the accuracy of prediction but also extends the time horizon of prediction to three years before failure. We also show that the earnings of banks are more significant in predicting bank failure than the capital structure and asset quality. The results further reveal that the CEO compensation, voting rights and institutional ownership are more significant predictors than the board characteristics. These results are robust when using logit regression. This paper provides insight to banks, regulators and shareholders by showing that corporate governance and banks earnings are strong predictors of bank failure

Financial soundness of Kazakhstan banks: analysis and prediction.

Purpose “ The financial systems in many emerging countries are still impacted by the devastating effect of the 2008 financial crisis which created a massive disaster in the global economy. The banking sector needs appropriate quantitative techniques to assess its financial soundness, strengths and weaknesses. This research aims to explore, empirically assess and analyze the financial soundness of the banking sector in Kazakhstan. It also examines the prediction of financial unsoundness at an individual bank level using PCA, cluster, MDA, logit and probit analyses. Design/Methodology/Approach “ A cluster analysis, in combination with principal component analysis (PCA), was utilized as a classification technique. It groups sound and unsound banks in Kazakhstan's banking sector by examining various financial ratios. Cluster analysis was run on a sample of 34 commercial banks on 1st January, 2008 and 37 commercial banks on 1st January, 2014 to test the ability of this technique to detect unsound banks before they fail. Then, Altman Z and EM Score models were tested and re-estimated and the MDA, logit and probit models were constructed on a sample of 12 Kazakhstan banks during the period between 1st January, 2008 and 1st January, 2014. The sample consists of 6 sound and 6 unsound banks and accounts for 81.3% of the total assets of the Kazakhstan banking sector in 2014. These statistical methods used various financial variables to represent capital adequacy, asset quality, management, earnings and liquidity. Last but not least, the MDA, logit and probit models were systematically combined together to construct an integrated model to predict bank financial unsoundness. Findings “ First of all, results from Chapter 3 indicate that cluster analysis is able to identify the structure of the Kazakh banking sector by the degree of financial soundness. Secondly, based on the findings in the second empirical chapter, the tested and re-estimated Altman models show a modest ability to predict bank financial unsoundness in Kazakhstan. Thirdly, the MDA, logit and probit models show high predictive accuracy in excess of 80%. Finally, the model that integrated the MDA, logit and probit types presents superior predictability with lower Type I errors. Practical Implications “ The results of this research are of interest to supervisory and regulatory bodies. The models can be used as a reliable and effective tool, particularly the cluster based methodology for assessing the degree of financial soundness in the banking sector and the integrated model for predicting the financial unsoundness of banks. Originality/Value “ This study is the first to employ a cluster-based methodology to assess financial soundness in the Kazakh banking sector. In addition, the integrated model can be used as a promising technique for evaluating the financial unsoundness of banks in terms of predictive accuracy and robustness. Importance “ Assessing the financial soundness of the Kazakh banking system is of particular importance as the World Bank has ranked Kazakhstan as leading the world for the volume of non-performing credits in the total number of loans granted in 2012. It is one of the first academic studies carried out on Kazakhstan banks which comprehensively evaluate the financial soundness of banks. It is anticipated that the findings of the current study will provide useful lessons for developing and transition countries during periods of financial turmoil

Climate Services for Resilient Development (CSRD) Partnership’s work in Latin America

The Climate Services for Resilient Development (CSRD) Partnership is a private-public collaboration led by USAID, which aims to increase resilience to climate change in developing countries through the development and dissemination of climate services. The partnership began with initial projects in three countries: Colombia, Ethiopia, and Bangladesh. The International Center for Tropical Agriculture (CIAT) was the lead organization for the Colombian CSRD efforts – which then expanded to encompass work in the whole Latin American region

Integrative Analysis to Investigate Complex Interaction in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative disorder featuring progressive cognitive and functional deficits. Pathologically, AD is characterized by tau and amyloid β protein deposition in the brain. As the sixth leading cause of death in the U.S., the disease course usually last from 7 to 10 years on average before the consequential death. In 2019 there are estimated 5.8 million Americans living with AD affecting 16 million family members. At certain stage of the disease course, patients with inability of maintaining their daily functioning highly depend on caregivers, primarily family caregivers, that incur estimated 18.4 billion unpaid hours of cares, which is equivalent to 232 billion dollars. These huge economic burdens and inevitable emotional distress on the family and the society would also increase as the number of AD affected population could triple by 2050. Altered cellular composition is associated with AD progression and decline in cognition, such as neuronal loss and astrocytosis, which is a key feature in neurodegeneration but has often been overlooked in transcriptome research. To explore the cellular composition changes in AD, I developed a deconvolution pipeline for bulk RNA-Seq to account for cell type specific effects in brain tissues. I found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies. These findings suggest that genetic risk factors associated with AD etiology have a specific effect on the cellular composition of AD brains. The digital deconvolution approach provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition. It also suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD. With deconvolution methods to delineate cell population changes in disease condition, it would help interpret transcriptomics results and reveal transcriptional changes in a cell type specific manner. One application demonstrated in this dissertation work is to use cell type proportion as quantitative trait to identify genetic factors associated with cellular composition changes. I performed cell type QTL analysis and identified a common pathway associated with neuronal protection underlying aging brains in the presence or absence of neurodegenerative disease symptoms. A protective variant of TMEM106B, which was previously identified with a protective effect in FTD, was identified to be associated with neuronal proportion in aging brains, suggesting a common pathway underlying neuronal protection and cognitive reservation in elderly. This extended analysis yield from deconvolution results demonstrated one promising direction of using deconvolution followed by cell type QTL analysis in identifying new genes or pathways underlying neurodegenerative or aging brains. To understand the complexity of the brain under disease condition, network analysis as a large-scale system-level approach provides unbiased and data-driven view to identify gene-gene interactions altered by disease status. Using network analysis, I replicated and reconfirmed the co-expression pattern between MS4A gene cluster and TREM2 in sporadic AD, from which further evidence was inferred from Bayesian network analysis to show that MS4A4A might be a potential regulator of TREM2 that is validated by in-vitro experiments. In Autosomal Dominant AD (ADAD) cohort, disrupted and acquired genes were identified from PSEN1 mutation carriers. Among these genes, previously identified AD risk genes and pathways were revealed along with novel findings. These results demonstrated the great potential of applying network approach in identifying disease associated genes and the interactions among them. To conclude the dissertation work from methodological, empirical, and theoretical levels, deconvolution pipeline for bulk RNA-Seq, cell type QTL analysis, and network analysis approaches were applied to understand transcriptome changes underlying disease etiology. From which previous AD related findings were replicated that validated the methods, and novel genes and pathways were identified as potential new therapeutic targets. Based on prior knowledge and empirical evidence observed from this dissertation work, a model is proposed to explain how genetic factors are assembled as a highly interconnected interactome network to affect proteinopathy observed in neurodegenerative disorders, that cause cellular composition changes in the brain, which ultimately leads to cognitive and functional deficits observed in AD patients

Applying advanced data analytics and machine learning to enhance the safety control of dams

The protection of critical engineering infrastructures is vital to today’s so- ciety, not only to ensure the maintenance of their services (e.g., water supply, energy production, transport), but also to avoid large-scale disasters. Therefore, technical and financial efforts are being continuously made to improve the safety control of large civil engineering structures like dams, bridges and nuclear facilities. This con- trol is based on the measurement of physical quantities that characterize the struc- tural behavior, such as displacements, strains and stresses. The analysis of monitor- ing data and its evaluation against physical and mathematical models is the strongest tool to assess the safety of the structural behavior. Commonly, dam specialists use multiple linear regression models to analyze the dam response, which is a well- known approach among dam engineers since the 1950s decade. Nowadays, the data acquisition paradigm is changing from a manual process, where measurements were taken with low frequency (e.g., on a weekly basis), to a fully automated process that allows much higher frequencies. This new paradigm escalates the potential of data analytics on top of monitoring data, but, on the other hand, increases data quality issues related to anomalies in the acquisition process. This chapter presents the full data lifecycle in the safety control of large-scale civil engineering infrastructures (focused on dams), from the data acquisition process, data processing and storage, data quality and outlier detection, and data analysis. A strong focus is made on the use of machine learning techniques for data analysis, where the common multiple linear regression analysis is compared with deep learning strategies, namely recur- rent neural networks. Demonstration scenarios are presented based on data obtained from monitoring systems of concrete dams under operation in Portugal.info:eu-repo/semantics/acceptedVersio

Analysing Financial Distress in Malaysian Islamic Banks: Exploring Integrative Predictive Methods

Against the background of global financial crisis, some argue in favour of the ‘resilience’ of Islamic finance, while others suggest that Islamic financial institutions are not more prone to distress and crisis than their conventional counterparts. However, there have been a number of cases of Islamic finance and banking distress in recent years, including instances in Malaysia. These cases, hence, motivated this study in terms of emphasising the importance of employing financial distress prediction models for analysing Islamic banks. This study aims at empirically exploring, examining and analysing the financial distress of the Malaysian Islamic banks. In doing so, the effectiveness of the existing early warning statistical insolvency prediction models that have been used in previous studies, and a particular model adapted by Islamic banks in Malaysia were critically evaluated. This study, hence, employed a number of models to predict the financial distress faced by Islamic banks in Malaysia. In addition, an attempt was made at the modification of the existing early warning insolvency prediction models in evaluating and analysing the financial distress of Malaysian Islamic banks. This research is constructed within four empirical chapters by employing three prediction models in assessing the financial distress of Islamic banks. The first empirical chapter analyses the secondary data collected from a sample of Islamic banks, based on selected ratios developed in the literature, whereby a comprehensive description of these selected financial ratios in terms of descriptive statistical analysis for the selected Islamic banks in Malaysia is provided. The second empirical chapter investigates the performance of the ‘emerging market Z-score’, introduced by Altman in predicting the performance of Islamic banks and conventional banks in Malaysia. The study aimed to introduce the EM Z-score as a valuable analytical tool in monitoring the deterioration of the performance of banks as well as looking at the impact of the global financial crisis on the performance of Islamic and conventional banks. This chapter examines thirteen Islamic banks and ten conventional banks during the period of 2005-2010. The results show that the EM Z-score for all banks is well above the cut-off point of 2.6, although for Islamic banks the EM Z-score showed a declining trend whilst for conventional banks it showed an increasing trend. This empirical evidence is important for the banks since it provides a warning signal to the banks’ management as well as the related parties involved in the planning, controlling and decision making process. The third empirical chapter presents the newly constructed integrated predictive model designed to evaluate and analyse the financial distress of Islamic banks in Malaysia, which can be used as an alternative model for regulators in monitoring the performance of Islamic banks that are experiencing any serious financial problems. This paper develops a preliminary model for the prediction of the performance level of Islamic financial institutions for the period of December 2005 to September 2010 by using quarterly data for ten selected Islamic banks in Malaysia. For this, factor analysis and three parametric models (discriminant analysis, logit analysis and probit analysis) are used. The results depict that the first few quarters before the benchmark quarter are the most important period for making a correct prediction and crucial decisions on the survival of Islamic banks. Thus, the results demonstrate the predictive ability of the integrated model to differentiate between the healthy and non-healthy Islamic banks, therefore reducing the expected cost of bank failure. The fourth empirical chapter conducts further exploration in predicting the financial distress position of Islamic banks by introducing new variables such as the funding structure, deposit composition, and macroeconomic variables. Using the same sample and data set for Islamic banks as in the previous chapter, this study shows the relationship between the banks’ funding profiles and other alternative variables, and the Islamic banks’ performance in Malaysia. For this, the logit model is used. Based on the results of all models, this study recommended two final models, which showed an excellent fit for predicting the Islamic banks’ performance. The results indicate that none of the macroeconomic variables included were significant, thus suggesting that the performance of Islamic banks in Malaysia was not affected by the economic conditions throughout the study period. This can perhaps be attributed to efficient regulation and supervision by the relevant authorities in the country

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy