Does lending behaviour of banks in emerging economies vary by ownership? Evidence from the Indian banking sector

While much has been discussed about the relationship between ownership and financial performance of banks in emerging markets, literature about cross-ownership differences in credit market behaviour of banks in emerging economies is sparse. Using a portfolio choice model and bank-level data from India for 9 years (1995–96 to 2003–04), we examine banks’ behaviour in the context of credit markets of an emerging market economy. Our results indicate that, in India, the data for the domestic banks fit well the aforementioned portfolio-choice model, especially for private banks, but the model cannot explain the behaviour of foreign banks. In general, allocation of assets between risk-free government securities and risky credit is affected by past allocation patterns, stock exchange listing (for private banks), risk averseness of banks, regulations regarding treatment of NPA, and ability of banks to recover doubtful credit. It is also evident that banks deal with changing levels of systematic risk by altering the ratio of securitized to non-securitized credit.

Laminar distribution of the pathological changes in sporadic frontotemporal lobar degeneration with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy:a quantitative study using polynomial curve fitting

Aims: Previous data suggest heterogeneity in laminar distribution of the pathology in the molecular disorder frontotemporal lobar degeneration (FTLD) with transactive response (TAR) DNA-binding protein of 43kDa (TDP-43) proteinopathy (FTLD-TDP). To study this heterogeneity, we quantified the changes in density across the cortical laminae of neuronal cytoplasmic inclusions, glial inclusions, neuronal intranuclear inclusions, dystrophic neurites, surviving neurones, abnormally enlarged neurones, and vacuoles in regions of the frontal and temporal lobe. Methods: Changes in density of histological features across cortical gyri were studied in 10 sporadic cases of FTLD-TDP using quantitative methods and polynomial curve fitting. Results: Our data suggest that laminar neuropathology in sporadic FTLD-TDP is highly variable. Most commonly, neuronal cytoplasmic inclusions, dystrophic neurites and vacuolation were abundant in the upper laminae and glial inclusions, neuronal intranuclear inclusions, abnormally enlarged neurones, and glial cell nuclei in the lower laminae. TDP-43-immunoreactive inclusions affected more of the cortical profile in longer duration cases; their distribution varied with disease subtype, but was unrelated to Braak tangle score. Different TDP-43-immunoreactive inclusions were not spatially correlated. Conclusions: Laminar distribution of pathological features in 10 sporadic cases of FTLD-TDP is heterogeneous and may be accounted for, in part, by disease subtype and disease duration. In addition, the feedforward and feedback cortico-cortical connections may be compromised in FTLD-TDP. © 2012 The Authors. Neuropathology and Applied Neurobiology © 2012 British Neuropathological Society.

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios. © 2011 Elsevier Inc.

Efficiency analysis of Islamic banks:a case of Gulf Cooperation Council (GCC)

Although considerable effort has been invested in the measurement of banking efficiency using Data Envelopment Analysis, hardly any empirical research has focused on comparison of banks in Gulf States Countries This paper employs data on Gulf States banking sector for the period 2000-2002 to develop efficiency scores and rankings for both Islamic and conventional banks. We then investigate the productivity change using Malmquist Index and decompose the productivity into technical change and efficiency change. Further, hypothesis testing and statistical precision in the context of nonparametric efficiency and productivity measurement have been used. Specially, cross-country analysis of efficiency and comparisons of efficiencies between Islamic banks and conventional banks have been investigated using Mann-Whitney test.

Statnote 36: Do the data fit the Poisson distribution

In previous Statnotes, many of the statistical tests described rely on the assumption that the data are a random sample from a normal or Gaussian distribution. These include most of the tests in common usage such as the ‘t’ test ), the various types of analysis of variance (ANOVA), and Pearson’s correlation coefficient (‘r’) . In microbiology research, however, not all variables can be assumed to follow a normal distribution. Yeast populations, for example, are a notable feature of freshwater habitats, representatives of over 100 genera having been recorded . Most common are the ‘red yeasts’ such as Rhodotorula, Rhodosporidium, and Sporobolomyces and ‘black yeasts’ such as Aurobasidium pelculans, together with species of Candida. Despite the abundance of genera and species, the overall density of an individual species in freshwater is likely to be low and hence, samples taken from such a population will contain very low numbers of cells. A rare organism living in an aquatic environment may be distributed more or less at random in a volume of water and therefore, samples taken from such an environment may result in counts which are more likely to be distributed according to the Poisson than the normal distribution. The Poisson distribution was named after the French mathematician Siméon Poisson (1781-1840) and has many applications in biology, especially in describing rare or randomly distributed events, e.g., the number of mutations in a given sequence of DNA after exposure to a fixed amount of radiation or the number of cells infected by a virus given a fixed level of exposure. This Statnote describes how to fit the Poisson distribution to counts of yeast cells in samples taken from a freshwater lake.

Advances in data envelopment analysis

Since its introduction in 1978, data envelopment analysis (DEA) has become one of the preeminent nonparametric methods for measuring efficiency and productivity of decision making units (DMUs). Charnes et al. (1978) provided the original DEA constant returns to scale (CRS) model, later extended to variable returns to scale (VRS) by Banker et al. (1984). These ‘standard’ models are known by the acronyms CCR and BCC, respectively, and are now employed routinely in areas that range from assessment of public sectors, such as hospitals and health care systems, schools, and universities, to private sectors, such as banks and financial institutions (Emrouznejad et al. 2008; Emrouznejad and De Witte 2010). The main objective of this volume is to publish original studies that are beyond the two standard CCR and BCC models with both theoretical and practical applications using advanced models in DEA.

Accounting for derivatives:an evaluation of reporting practice by UK banks

In 1998 the Accounting Standards Board (ASB) published FRS 13, ‘Derivatives and other Financial Instruments: Disclosures’. This laid down the requirements for disclosures of an entity’s policies, objectives and strategies in using financial instruments, their impact on its risk, performance and financial condition, and details of how risks are managed. FRS 13 became effective in March 1999, and this paper uses the 1999 annual reports of UK banks to evaluate the usefulness of disclosures from a user’s perspective. Usefulness is measured in terms of the criteria of materiality, relevance, reliability, comparability and understandability as defined in the ASB’s Statement of Principles (ASB, 1999). Our findings suggest that the narrative disclosures are generic in nature, the numerical data incomplete and not always comparable, and that it is difficult for the user to combine both narrative and numerical information in order to assess the banks’ risk profile. Our overall conclusion is therefore that current UK financial reporting practices are of limited help to users wishing to assess the scale of an institution’s financial risk exposure.

Discovering properties of new DNA-binding activity of proteins

Protein-DNA interactions are an essential feature in the genetic activities of life, and the ability to predict and manipulate such interactions has applications in a wide range of fields. This Thesis presents the methods of modelling the properties of protein-DNA interactions. In particular, it investigates the methods of visualising and predicting the specificity of DNA-binding Cys2His2 zinc finger interaction. The Cys2His2 zinc finger proteins interact via their individual fingers to base pair subsites on the target DNA. Four key residue positions on the a- helix of the zinc fingers make non-covalent interactions with the DNA with sequence specificity. Mutating these key residues generates combinatorial possibilities that could potentially bind to any DNA segment of interest. Many attempts have been made to predict the binding interaction using structural and chemical information, but with only limited success. The most important contribution of the thesis is that the developed model allows for the binding properties of a given protein-DNA binding to be visualised in relation to other protein-DNA combinations without having to explicitly physically model the specific protein molecule and specific DNA sequence. To prove this, various databases were generated, including a synthetic database which includes all possible combinations of the DNA-binding Cys2His2 zinc finger interactions. NeuroScale, a topographic visualisation technique, is exploited to represent the geometric structures of the protein-DNA interactions by measuring dissimilarity between the data points. In order to verify the effect of visualisation on understanding the binding properties of the DNA-binding Cys2His2 zinc finger interaction, various prediction models are constructed by using both the high dimensional original data and the represented data in low dimensional feature space. Finally, novel data sets are studied through the selected visualisation models based on the experimental DNA-zinc finger protein database. The result of the NeuroScale projection shows that different dissimilarity representations give distinctive structural groupings, but clustering in biologically-interesting ways. This method can be used to forecast the physiochemical properties of the novel proteins which may be beneficial for therapeutic purposes involving genome targeting in general.

Market risk reporting by the world's top banks:evidence on the diversity of reporting practice and the implications for international accounting harmonisation

The increasing adoption of international accounting standards and global convergence of accounting regulations is frequently heralded as serving to reduce diversity in financial reporting practice. In a process said to be driven in large part by the interests of international business and global financial markets, one might expect the greatest degree of convergence to be found amongst the world’s largest multinational financial corporations. This paper challenges such claims and presumptions. Its content analysis of longitudinal data for the period 2000-2006 reveals substantial, on going diversity in the market risk disclosure practices, both numerical and narrative, of the world’s top-25 banks. The significance of such findings is reinforced by the sheer scale of the banking sector’s risk exposures that have been subsequently revealed in the current global financial crisis. The variations in disclosure practices documented in the paper apply both across and within national boundaries, leading to a firm conclusion that, at least in terms of market risk reporting, progress towards international harmonisation remains rather more apparent than real.

A DNA Codification for Genetic Algorithms Simulation

In this paper we propose a model of encoding data into DNA strands so that this data can be used in the simulation of a genetic algorithm based on molecular operations. DNA computing is an impressive computational model that needs algorithms to work properly and efficiently. The first problem when trying to apply an algorithm in DNA computing must be how to codify the data that the algorithm will use. In a genetic algorithm the first objective must be to codify the genes, which are the main data. A concrete encoding of the genes in a single DNA strand is presented and we discuss what this codification is suitable for. Previous work on DNA coding defined bond-free languages which several properties assuring the stability of any DNA word of such a language. We prove that a bond-free language is necessary but not sufficient to codify a gene giving the correct codification.

Identification of DNA-binding proteins by incorporating evolutionary information into pseudo amino acid composition via the top-n-gram approach

DNA-binding proteins are crucial for various cellular processes and hence have become an important target for both basic research and drug development. With the avalanche of protein sequences generated in the postgenomic age, it is highly desired to establish an automated method for rapidly and accurately identifying DNA-binding proteins based on their sequence information alone. Owing to the fact that all biological species have developed beginning from a very limited number of ancestral species, it is important to take into account the evolutionary information in developing such a high-throughput tool. In view of this, a new predictor was proposed by incorporating the evolutionary information into the general form of pseudo amino acid composition via the top-n-gram approach. It was observed by comparing the new predictor with the existing methods via both jackknife test and independent data-set test that the new predictor outperformed its counterparts. It is anticipated that the new predictor may become a useful vehicle for identifying DNA-binding proteins. It has not escaped our notice that the novel approach to extract evolutionary information into the formulation of statistical samples can be used to identify many other protein attributes as well.

The role of DNA methylation in aging, rejuvenation, and age-related disease

DNA methylation is a major control program that modulates gene expression in a plethora of organisms. Gene silencing through methylation occurs through the activity of DNA methyltransferases, enzymes that transfer a methyl group from S-adenosyl-l-methionine to the carbon 5 position of cytosine. DNA methylation patterns are established by the de novo DNA methyltransferases (DNMTs) DNMT3A and DNMT3B and are subsequently maintained by DNMT1. Aging and age-related diseases include defined changes in 5-methylcytosine content and are generally characterized by genome-wide hypomethylation and promoter-specific hypermethylation. These changes in the epigenetic landscape represent potential disease biomarkers and are thought to contribute to age-related pathologies, such as cancer, osteoarthritis, and neurodegeneration. Some diseases, such as a hereditary form of sensory neuropathy accompanied by dementia, are directly caused by methylomic changes. Epigenetic modifications, however, are reversible and are therefore a prime target for therapeutic intervention. Numerous drugs that specifically target DNMTs are being tested in ongoing clinical trials for a variety of cancers, and data from finished trials demonstrate that some, such as 5-azacytidine, may even be superior to standard care. DNMTs, demethylases, and associated partners are dynamically shaping the methylome and demonstrate great promise with regard to rejuvenation. © Copyright 2012, Mary Ann Liebert, Inc. 2012.

A new inverse DEA method for merging banks

This study suggests a novel application of Inverse Data Envelopment Analysis (InvDEA) in strategic decision making about mergers and acquisitions in banking. The conventional DEA assesses the efficiency of banks based on the information gathered about the quantities of inputs used to realize the observed level of outputs produced. The decision maker of a banking unit willing to merge/acquire another banking unit needs to decide about the inputs and/or outputs level if an efficiency target for the new banking unit is set. In this paper, a new InvDEA-based approach is developed to suggest the required level of the inputs and outputs for the merged bank to reach a predetermined efficiency target. This study illustrates the novelty of the proposed approach through the case of a bank considering merging with or acquiring one of its competitors to synergize and realize higher level of efficiency. A real data set of 42 banking units in Gulf Corporation Council countries is used to show the practicality of the proposed approach.

Mitochondrial DNA, segments I and II, its applications in population genetics and forensic sciences

The investigations of human mitochondrial DNA (mtDNA) have considerably contributed to human evolution and migration. The Middle East is considered to be an essential geographic area for human migrations out of Africa since it is located at the crossroads of Africa, and the rest of the world. United Arab Emirates (UAE) population inhabits the eastern part of Arabian Peninsula and was investigated in this study. Published data of 18 populations were included in the statistical analysis. The diversity indices showed (1) high genetic distance among African populations and (2) high genetic distance between African populations and non-African populations. Asian populations clustered together in the NJ tree between the African and European populations. MtDNA haplotypes database of the UAE population was generated. By incorporating UAE mtDNA dataset into the existing worldwide mtDNA database, UAE Forensic Laboratories will be able to analyze future mtDNA evidence in a more significant and consistent manner. ^

Knowledge discovery using multiple sources of biological data

The primary aim of this dissertation is to develop data mining tools for knowledge discovery in biomedical data when multiple (homogeneous or heterogeneous) sources of data are available. The central hypothesis is that, when information from multiple sources of data are used appropriately and effectively, knowledge discovery can be better achieved than what is possible from only a single source. ^ Recent advances in high-throughput technology have enabled biomedical researchers to generate large volumes of diverse types of data on a genome-wide scale. These data include DNA sequences, gene expression measurements, and much more; they provide the motivation for building analysis tools to elucidate the modular organization of the cell. The challenges include efficiently and accurately extracting information from the multiple data sources; representing the information effectively, developing analytical tools, and interpreting the results in the context of the domain. ^ The first part considers the application of feature-level integration to design classifiers that discriminate between soil types. The machine learning tools, SVM and KNN, were used to successfully distinguish between several soil samples. ^ The second part considers clustering using multiple heterogeneous data sources. The resulting Multi-Source Clustering (MSC) algorithm was shown to have a better performance than clustering methods that use only a single data source or a simple feature-level integration of heterogeneous data sources. ^ The third part proposes a new approach to effectively incorporate incomplete data into clustering analysis. Adapted from K-means algorithm, the Generalized Constrained Clustering (GCC) algorithm makes use of incomplete data in the form of constraints to perform exploratory analysis. Novel approaches for extracting constraints were proposed. For sufficiently large constraint sets, the GCC algorithm outperformed the MSC algorithm. ^ The last part considers the problem of providing a theme-specific environment for mining multi-source biomedical data. The database called PlasmoTFBM, focusing on gene regulation of Plasmodium falciparum, contains diverse information and has a simple interface to allow biologists to explore the data. It provided a framework for comparing different analytical tools for predicting regulatory elements and for designing useful data mining tools. ^ The conclusion is that the experiments reported in this dissertation strongly support the central hypothesis.^