Chrysovalantis Gaganis

Probabilistic Neural Networks for the Identification of Qualified Audit Opinions

Prior studies that examine the application of neural networks in auditing investigate the efficiency of artificial neural networks (ANNs). In the present study, considering the well known disadvantages of artificial neural network, we propose the application of probabilistic neural networks (PNNs) that combine the computational power and flexibility of ANNs, while managing to retain simplicity and transparency. The sample consists of 264 financial statements that received a qualified audit opinion over the period 1997-2004 and 3069 unqualified ones, from 881 firms listed on the London Stock Exchange. The results demonstrate the high explanatory power of the PNN model in explaining qualifications in audit reports. The model is also found to outperform traditional ANN models, as well as logistic regression. Sensitivity analysis is used to assess the relative importance of the input variables and to analyze their role in the auditing process.

Estimating and analyzing the efficiency and productivity of bank branches: Evidence from Greece

Purpose - The purpose of this paper is to examine the efficiency and productivity of a Greek banks branches. Design/methodology/approach - The sample consists of 458 branches of a Greek commercial bank, operating in 13 regions of Greece over the period 2002-2005, a total of 1,795 observations. Data envelopment analysis was used to explore the efficiency and productivity of the branches. Then, fixed and random effects models were used to determine the impact of internal and external factors on the efficiency and productivity scores. Findings - The results indicate that the branches in the sample could have achieved improved overall performance during 2002-2005. Also, that the inclusion of loan loss provisions as an input variable increases the efficiency score, but for the total factor productivity (TFP) change, the results are mixed. The second stage regressions indicate that both the logarithm of personnel and the logarithm of income per capita in the local market have a significant impact on efficiency, while the loans to total assets ratio has a significant impact on pure technical efficiency only. When the various productivity change measures were regressed over the explanatory variables, it was found that the logarithm of per capita gross fixed capital formation has a positive and statistically significant impact on all measures. Also, that the return on assets, the loans to deposit ratio, the logarithm of personnel, and the logarithm of income of per capita, all have a positive and statistically significant impact on overall efficiency change. Originality/value - This paper is the first study on Greek branches which examines the impact of market conditions. It examines the impact of risk-taking on the efficiency of the branches and examines the productivity growth of the branch network using the Malmquist TFP index.

What Drives Acquisitions in the EU Banking Industry? The Role of Bank Regulation and Supervision Framework, Bank Specific and Market Specific Factors

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.

Financial Characteristics of Banks Involved in Acquisitions: Evidence from Asia

This study examines the financial characteristics of 52 targets and 47 acquirers that were involved in acquisitions in the Asian commercial banking sector over the period 1998 to 2004 and a control sample of non-merged banks matched by country and year. Three logistic regression models are estimated to determine the factors that influence the probability of being involved in an acquisition either as a target or as an acquirer. The results indicate that more asset risky portfolios increase this probability. Higher liquidity also increases the probability of being acquired. The probability of being involved in an acquisition as acquirer also increases with size and cost efficiency. Finally, more profitable banks are more likely to be involved in acquisitions as acquirers rather than as targets. When we partition our sample in two sub-periods we find that only the higher loan loss provisions of targets and the higher size of acquirers remain robust over time.

Explaining Qualifications in Audit Reports Using a Support Vector Machine Methodology

The verification of whether the financial statements of a firm represent its actual position is of major importance for auditors, which should provide a qualified report if they conclude that the financial statements fail to meet this requirement. This paper implements support vector machines (SVM) to develop models that may support auditors in this task. Linear and non-linear models are developed and their performance is analyzed 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.

Efficiency in the Greek Banking Industry: A Comparison of Foreign and Domestic Banks

Abstract 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.

Bank Diversification and Overall Financial Strength: International Evidence

There are many studies in the finance and management literature that examine the impact of diversification on performance. Yet, the literature remains inconclusive as for the potential benefits in terms of risk and return. The present study aims to re‐examine this issue, while proposing a methodological framework that integrates various bank performance and risk indicators into a single measure of financial strength. Using an international sample of commercial banks, we find that diversification in terms of income, earning assets, and on‐ and off‐balance sheet activities influences positively their financial strength. We also find that income diversification can be more beneficial for banks operating in less developed countries compared to banks in advanced and major advanced economies. However, we observe the opposite in the case of diversification between off‐balance sheet and on‐balance sheet activities. Furthermore, the results reveal that income and earning assets diversification can mitigate the adverse effect of the financial crisis on bank financial strength. We continue to find a positive relationship between diversification and financial strength when we account for nesting effects, endogeneity, as well as when using an alternative approach for the construction of the financial strength indicator.

Modelling banking sector stability with multicriteria approaches

Banking crises can be damaging for the economy, and as the recent experience has shown, nowadays they can spread rapidly across the globe with contagious effects. Therefore, the assessment of the stability of a county’s banking sector is important for regulators, depositors, investors and the general public. In the present study, we propose the development of classification models that assign the banking sectors of various countries in three classes, labelled “low stability”, “medium stability”, and “high stability”. The models are developed using three multicriteria decision aid techniques, which are well-suited to ordinal classification problems. We use a sample of 114 banking sectors (i.e., countries), and a set of criteria that includes indicators of the macroeconomic, institutional and regulatory environment, as well as basic characteristics of the banking and financial sector. The models are developed and tested using a tenfold cross-validation approach and they are benchmarked against models developed with discriminant analysis and logistic regression.

The Effect of Board Directors from Countries with Different Genetic Diversity Levels on Corporate Performance

We link genetic diversity in the country of origin of the firms’ board members with corporate performance via board members’ nationality. We hypothesize that our approach captures deep-rooted differences in cultural, institutional, social, psychological, physiological, and other traits that cannot be captured by other recently measured indices of diversity. Using a panel of firms listed in the North American and U.K. stock markets, we find that adding board directors from countries with different levels of genetic diversity (either higher or lower) increases firm performance. This effect prevails when we control for a number of cultural, institutional, firm-level, and board member characteristics, as well as for the nationality of the board of directors. To identify the relationship, we use as instrumental variables for our diversity indices the migratory distance from East Africa and the level of ultraviolet exposure in the directors’ country of nationality.

An Application of Support Vector Machines in the Prediction of Acquisition Targets: Evidence from the EU Banking Sector

In this paper, we investigate the relative performance of both linear and non-linear support vector machines (SVMs) models with a polynomial and an RBF kernel in the development of classification models for the prediction of EU bank acquisition targets. The training sample consists of 274 banks, half of which were acquired between 1998 and 2001. The validation sample consists of 31 banks acquired during 2002, and 429 non-acquired banks. We use eight financial variables reflecting the following bank characteristics: capital strength, profitability, efficiency in expenses management, loan activity, liquidity, size, growth, and market power. The models are evaluated in terms of their classification accuracy, as well as with ROC analysis. In both cases, the differences between the models are only marginal.