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, 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.     

On the Evaluation of Greek Industrial SME's Performance via Multicriteria Analysis of Financial Ratios

The objective of this article is to propose a methodological framework for developing evaluation models for estimating SMEs' performance, based on financial ratio analysis. The proposed framework involves the application of a multiple criteria decision aid method, namely the UTADIS method (UTilités Additives DIScriminantes), to develop a decision model that classifies the SMEs into predefined homogeneous classes according to their performance. The applicability and the efficiency of this approach are examined using financial data on Greek industrial SMEs for the period 1988–1996. A comparison with well-known multivariate and econometric techniques (i.e., discriminant, logit, probit analysis) is also undertaken for comparative purposes.     

A multicriteria discrimination approach for the credit rating of Asian banks

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.      

Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece

The purpose of this paper is to test the extent to which client (corporate) performance measures can be used to enhance the ability to discriminate between the choice of a qualified or unqualified (clean) audit report. Audit firms face the risk of losing the client if they issue a qualification. On the other hand, failing to qualify exposes the auditor to potential lawsuits and loss of reputation. We examined the financial statements, auditors' opinions, and financial statements notes for companies in Greece that received a qualified audit report and for those that received an unqualified audit report. We modeled the auditor's qualification using a multicriteria decision aid classification method (UTADIS—UTilités Additives Discriminates) and compared it with other multivariate statistical techniques such as discriminant and logit analysis. The qualification decision is explained by financial ratios and by nonfinancial information such as the client litigation. The developed models are accurate in classifying the total sample correctly with rates of almost 80%.     

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.     

Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques

Falsifying financial statements involves the manipulation of financial accounts by overstating assets, sales and profit, or understating liabilities, expenses or losses. This paper explores the effectiveness of an innovative classification methodology in detecting firms that issue falsified financial statements (FFS) and the identification of the factors associated to FFS. The methodology is based on the concepts of multicriteria decision aid (MCDA) and the application of the UTADIS classification method (UTilités Additives DIScriminantes). A sample of 76 Greek firms (38 with FFS and 38 non-FFS) described over ten financial ratios is used for detecting factors associated with FFS. A jackknife procedure approach is employed for model validation and comparison with multivariate statistical techniques, namely discriminant and logit analysis. The results indicate that the proposed MCDA methodology outperforms traditional statistical techniques which are widely used for FFS detection purposes. Furthermore, the results indicate that the investigation of financial information can be helpful towards the identification of FFS and highlight the importance of financial ratios such as the total debt to total assets ratio, the inventories to sales ratio, the net profit to sales ratio and the sales to total assets ratio.