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Summarization: There exists a lively debate as for the appropriate architecture of the financial supervision regime, with a long list of theoretical advantages and disadvantages associated with each one of its key dimensions. The present study investigates whether and how bank profit efficiency is influenced by the central bank’s involvement in financial supervision, the unification of financial authorities, and the independence of the central bank. The results show that efficiency decreases as the number of the financial sectors that are supervised by the central bank increases. Additionally, banks operating in countries with greater unification of supervisory authorities are less profit efficient. Finally, central bank independence has a negative impact on bank profit efficiency. Presented on: Journal of Banking and Finance

Summarization: This study aims to examine whether and how bank efficiency estimates are influenced by the shift from local GAAP to IFRS in EU. We use financial statements prepared during the transition period, and stochastic frontier analysis to obtain cost and profit efficiency estimates for a sample of 141 banks from 15 countries. Our main finding is that the bank efficiency estimates have been significantly influenced by the transition to IFRS. However, different consequences are reported for profit and cost efficiency. The paper offers some interesting conclusions on bank efficiency estimates’ variation due to an important change in the accounting rules. Presented on: International Review of Financial Analysis

Summarization: We use country level data and bank level data from 71 countries and 857 banks to investigate the impact of bank regulations, supervision, market structure, and bank characteristics on individual bank ratings. The results indicate that less cost efficient banks, with higher than average levels of provisions relatively to their income, and lower liquidity tend to have lower ratings. Larger and more profitable banks tend to obtain higher ratings. Higher equity to assets ratio results in higher ratings only when we do not control for bank supervision and regulations. Capital requirements, restrictions on bank activities, official disciplinary power, explicit deposit insurance scheme, higher deposit insurer power, liquidity and diversification guidelines, entry requirements, fraction of entries denied, and economic freedom have a significant impact on ratings in all of our specifications. Disclosure requirements and foreign banks entry have a significant impact on ratings only when we simultaneously control for the regulatory environment and the market structure, while auditing requirements have a significant impact only when we control for the regulatory environment alone. Finally, banks in developed countries are assigned higher ratings. However, this impact disappears when we include the regulatory and supervision variables in the models Παρουσιάστηκε στο: Review of Quantitative Finance and Accounting

Summarization: This study investigates the efficiency of k-nearest neighbours (k-NN) in developing models for estimating auditors' opinions, as opposed to models developed with discriminant and logit analyses. The sample consists of 5276 financial statements, out of which 980 received a qualified audit opinion, obtained from 1455 private and public UK companies operating in the manufacturing and trade sectors. We develop two industry-specific models and a general one using data from the period 1998–2001, which are then tested over the period 2002–2003. In each case, two versions of the models are developed. The first includes only financial variables. The second includes both financial and non-financial variables. The results indicate that the inclusion of credit rating in the models results in a considerable increase both in terms of goodness of fit and classification accuracies. The comparison of the methods reveals that the k-NN models can be more efficient, in terms of average classification accuracy, than the discriminant and logit models. Finally, the results are mixed concerning the development of industry-specific models, as opposed to general models. Presented on: Intelligent Systems in Accounting, Finance and Management

Summarization: This study presents the first attempt to develop classification models for the prediction of share repurchase announcements using multicriteria decision aid (MCDA) techniques. We use three samples consisting of 434 UK firms, 330 French firms, and 296 German firms, to develop country-specific models. The MCDA techniques that are applied for the development of the models are the UTilités Additives DIScriminantes (UTADIS) and the ELimination and Choice Expressing REality (ELECTRE) TRI. We adopt a 10-fold cross validation approach, a re-sampling technique that allows us to split the datasets in training and validation sub-samples. Thus, at the first stage of the analysis the aim is the development of a model capable of reproducing the classification of the firms considered in the training samples. Once this stage is completed, the model can be used for the classification of new firms not included in the training samples (i.e. validation stage). The results show that both MCDA models achieve quite satisfactory classification accuracies in the validation sample and they outperform both logistic regression and chance predictions. The developed models could provide the basis for a decision tool for various stakeholders such as managers, shareholders, and investment analysts. Presented on: Omega

Summarization: The purpose of this study is to estimate and explain the performance of non-life (i.e., property and casualty) insurers. The analysis consists of two stages. First, we propose the use of a multicriteria method to assess the condition of insurers while considering simultaneously a set of conflicting financial criteria. Then, we use regression analysis to examine the influence of firm-specific and country-specific attributes on the overall measure of performance obtained during the first stage. Macroeconomic conditions such as gross domestic product (GDP) growth, inflation, and income inequality are the most robust predictors of performance. However, other country-specific characteristics that relate to the institutional environment and financial or economic freedom do not appear to matter. Παρουσιάστηκε στο: JCC: The Business and Economics Research Journal

Summarization: The verification of whether the financial statements of a firm represent its actual position is of major importance for auditors, who should provide a qualified report if they conclude that the financial statements fail to meet this requirement. This paper implements support vector machines (SVMs) to develop models that may support auditors in this task. Linear and non-linear models are developed and their performance is analysed using training samples of different size and out-of-sample/out-of-time data. The results show that all SVM models are capable of distinguishing between qualified and unqualified financial statements with satisfactory accuracy. The performance of the models over time is also explored. Copyright © 2005 John Wiley & Sons, Ltd. Παρουσιάστηκε στο: Int. Syst. in Accounting, Finance and Management

Summarization: This paper provides the first cross-country study on the association between firms' soundness and regulatory policies in the insurance industry. Measuring solvency with an accounting based measure of distance to default, namely the Z-score, we find that the power of the supervisory authorities, and regulations related to both technical provisions and investments have an impact on soundness that is robust to controls for firm-specific and country-specific factors. In contrast, corporate governance and internal control rules do not influence soundness. Similarly, capital requirements do not appear to have a robust impact on soundness. Presented on: Jindal Journal of Business Research

Summarization: We investigate the association between the regulatory and supervision framework and the likelihood of being involved in acquisitions either as acquirer or target in the Asian banking industry over the period 1998–2004. The sample consists of 473 commercial banks operating in 9 markets, a total of 2,693 yearly observations. We use information from the new database of the World Bank and examine various regulations such as the level of accounting and information disclosure requirements, the restrictions on bank activities, the degree of official disciplinary power, and capital adequacy requirements. Our results suggest that the latter two influenced the probability of becoming a target or acquirer; however, restriction on activities and disclosure requirements had no impact on the acquisition likelihood. Presented on: Asia-Pacific Financial Markets

Summarization: We investigate the determinants of commercial bank acquisitions in the former fifteen countries of the European Union by evaluating the impact of bank-specific measures, such as size, growth and efficiency of banks, and external influences reflecting industry level differences in the regulatory and supervision framework, market environment and economic conditions. Our empirical analysis involves multinomial logit estimation at various levels in order to identify those characteristics that most consistently predict targets and acquirers from a sample of over 1400 commercial banks. The overall results indicate that, relative to banks that were not involved in the acquisitions, (i) targets and acquirers were significantly larger, less well capitalized and less cost efficient, (ii) targets were less profitable with lower growth prospects, and acquirers more profitable with higher growth prospects, (iii) external factors have affected targets and acquirers differently, and their effects have not been consistent or robust to sample size changes. Presented on: Financial Markets, Institutions and Instruments

Summarization: This study uses a sample of foreign and domestic banks operating in Greece during 1999–2004 to examine the impact of ownership on efficiency. We estimate an input oriented data envelopment analysis (DEA) model under variable returns to scale with inputs and outputs selected on the basis of a profit‐oriented approach. The results indicate an average pure technical efficiency equal to 0.7325 showing that the banks in sample could improve their efficiency by 26.75%. Over the same period, scale efficiency was equal to 0.6830. The comparison of the efficiency scores by group of ownership shows that domestic banks have higher pure technical efficiency and lower scale efficiency; however, the differences are not statistically significant. A DEA window‐analysis confirms the results of the cross‐section estimations. We also estimate a Tobit regression model but consistent with the univariate results we find no evidence to support the argument that ownership has a statistically significant impact on efficiency. Presented on: International Journal of the Economics of Business

Summarization: The present study investigates the efficiency of four classification techniques, namely discriminant analysis, logit analysis, UTADIS multicriteria decision aid, and nearest neighbours, in the development of classification models that could assist auditors during the examination of Asian commercial banks. To develop the auditing models and examine their classification ability, the dataset is split into two distinct samples. The training sample consists of 1,701 unqualified financial statements and 146 ones that received a qualified opinion over the period 1996–2001. The models are tested in a holdout sample of 527 unqualified financial statements and 52 ones that received a qualified opinion over the period 2002–2004. The results show that the developed auditing models can discriminate between financial statements that should receive qualified opinions from the ones that should receive unqualified opinions with an out-of-sample accuracy around 60%. The highest classification accuracy is achieved by UTADIS, followed by logit analysis, nearest neighbours and discriminant analysis. Both financial variables and the environment in which banks operate appear to be important factors. Presented on: Operational Research, An International Journal

Summarization: In this paper we develop classification models that could assist auditors in their decision to issue a qualified or unqualified opinion during the auditing of EU credit institutions. The models are developed with the Utilites Additives Discriminantes (UTADIS) multicriteria technique and incorporate financial and non-financial variables. Discriminant analysis is also employed for comparison purposes. The dataset consists of 80 qualified financial statements and 2,631 unqualified ones, from 446 credit institutions operating in eight EU countries over the period 1995–2002. The models are developed and tested using a ten-fold cross-validation approach. The results show that the developed auditing models can discriminate between financial statements that should receive qualified opinions from the ones that should receive unqualified opinions. The overall accuracy is 73.55% in the validation sample for a general model and increases up to 88.49% in the case of a model developed specifically for commercial banks. Presented on

Summarization: This study investigates whether the capital market values the efficiency of firms. After tracing stock returns and efficiency changes of 399 listed insurance firms in 52 countries during the 2002–2008 period, the paper reports a positive and statistically significant relationship between profit efficiency change and market adjusted stock returns. However, there is no robust evidence that cost efficiency change is associated with stock returns. Presented on: Journal of Productivity Analysis

Summarization: This paper investigates the role of technical reserves in the income smoothing behavior of insurance companies. This is one of the first attempts in the literature to trace such relationship in the insurance industry, especially at a multi-country setting. The experience of 770 insurance firms operating in 87 countries over the period 2000-2009 reveals that there is a significant evidence of income smoothing. The paper also finds that institutional characteristics, e.g., the rule of law, common law legal origin, economic freedom, and regulations relating to technical provisions and supervisory power constrain income smoothing but other factors such as capital requirements, tax deductibility of provisions, auditing, and corporate governance do not have a significant effect. Presented on: Omega

Summarization: In this paper we develop a multicriteria decision aid model, to investigate whether it is possible to replicate the credit ratings of Fitch on Asian banks using publicly available data. The model is developed with the Multi-group Hierarchical DIScrimination (MHDIS) approach, following a tenfold cross validation procedure. Five financial variables are selected from a list of nineteen ones through factor analysis. An additional set of five non-financial variables covering ownership, corporate governance, auditing, strength of bank’s franchise and its banking environment is also being used. The results show that equity/customer and short term funding, net interest margin and return on average equity are the most important financial variables. The number of shareholders, the number of subsidiaries and the banking environment of the country in which the banks operate are the most important non-financial ones. In terms of classification accuracies, the results show that the MHDIS model can replicate the credit ratings of Fitch with a satisfactory accuracy and is more efficient than discriminant analysis and ordered logistic regression that are used for comparison purposes. Presented on: Annals of Finance

Summarization: In recent years, there has been an increase in the number and value of securities class actions (SCAs), attracting the attention of various stakeholders such as investors, managers, policy makers, lawyers, etc. The present study extends the literature, by investigating for the first time the development of a classification model to forecast SCAs filed against US banks. Our results show that the proposed multicriteria decision aid model achieves a satisfactory accuracy, by classifying correct around 80 % of the banks in an out-of-sample testing. We obtain similar results when we use a walk-forward approach, instead of a tenfold cross-validation technique, for the estimation and testing of the model. Further analysis indicates that the classification accuracies can improve further by the inclusion of a corporate governance indicator that relates to executive and director compensation and ownership. Presented on: OR Spectrum

Summarization: In the present study, we investigate the explanatory power of information such as auditors' opinion and auditors' size in predicting impending bankruptcy for companies operating in the Greek market. Using nine financial ratios and two dummy variables, covering all dimensions of firms' financial performance, we initially develop two classification technique based models, Artificial Neural Networks (ANN) and Discriminant Analysis (DA). Our main purpose is to find out if the integration of the variables of audit opinion and auditor size in the initial models increases their ability in predicting impending bankruptcy. A comparison of the prediction accuracy of these two models before and after the integration of the additional informational variables is also included. The results indicate that both models achieve more satisfactory classification accuracy in discriminating bankrupted and non-bankrupted firms when the variables of audit opinion, auditors' are incorporated in the analysis compared to the use of financial ratios, only. Presented on: International Journal of Financial Services Management

A dissertation submitted to the School of Production Engineering and Management at the Technical University of Crete in partial fulfilment of the requirements for the degree of Doctor of Philosophy. Summarization: This thesis is a comprehensive and complete research on bank failure prediction, as it examines various modeling aspects for obtaining improved results. The analysis is based on a comprehensive dataset of approximately 60,000 observations over an extensive period of nine years (2005-2014), and it examines different prediction horizons, for up to three years prior to failure. We explore whether the addition of variables related to the diversification of the banks’ activities, along with local effects, improves the predictability of the models. Seven popular and widely used machine-learning techniques are compared (logistic regression, support vector machines with linear and radial kernels, naïve Bayes, extreme gradient boosting, random forests and artificial neural networks) and three different classification performance metrics are calculated (AUROC, H-measure, and Kolmogorov-Smirnov metric). In order to ensure the robustness of the results, bootstrap testing is used. The results show that mid- and long-range predictions improve significantly with the addition of diversification variables. Local effects exist and further improve the results while support vector machines along with gradient boosting and random forests outperform the traditional models with the differences increasing over longer prediction horizons.

This paper investigates the relationship between interbank funds and efficiencies is for the commercial banks operating in Turkey between 2001-2006. Data Envelopment Analysis (DEA) is executed to find the efficiency scores of the banks for each year, and fixed effects panel data regression is carried out, with the efficiency scores being the response variable. It is observed that interbank funds (ratio) has negative effects on bank efficiency, while bank capitalization and loan ratio have positive, and profitability has insignificant effects. Our study serves as an illustrative evidence that interbank funds can have adverse effects in an emerging market.