The Possibility of Predictions in Auditor’s Opinion: The Case of the Serbian Tobacco Industry

Research Questions: This paper investigated whether there is a possibility for predictions of an auditor’s opinion that can be used to predict, in an extremely accurate way, future developments in one company. Motivation: The research of Dopuch, Holhausen and Leftwich (1987); Kirkos, Spathis and Manolopoulos (2007) or Kirskos (2012) and Kim and Upneja (2014) open space for new challenges for using auditing methods.The most trying task is to find a technique that will be able to timely, accurately and with the least waste of resources respond to the challenge. The fact that auditors are forced to expand the scope and purpose of the audit work, respecting new risks that are continually changing represents the primary inspiration for this paper. Idea: Our goal was to explore whether one of the possible techniques for prediction the auditor’s opinion – multivariate discriminant analysis – can precisely predict a correct future audit opinion and whether this analysis is useful for finding solutions to performing predictions. Data: The analysis was conducted using data from financial statements of 4 Serbian tobacco companies of years 2011, 2012, 2013, 2014 and 2015 published by the Serbian Business Registers Agency. Tools: The presented research, based on theoretical and mathematical support, uses statistical software tools Statistica. Findings: The application of discriminant analysis in Serbian tobacco companies showed statistically major variables of the balance sheet, manely “Intangible assets", "Supplies" and "Liabilities". Following these variables, we obtained results which we used as the predictors. The outcome of our preliminary investigation presented accurate and correct prediction which is also confirmed by historical data. The result of this investigation can be used for further more complex investigations when using some variables that will lead to discriminatory analysis for more classification groups to mark and rank the most significant variables for expressing the audit opinion. Contribution: Provided information is important for every business, because every entity that is listed on the business market aims to be as better as possible, and find out and exploit the possibility of avoiding a negative result

Integrated performance measurement system for Slovak heating industry: A balanced scorecard approach

The prerequisite for businesses’ success, competitiveness, and non-bankruptcy is their performance. An effective performance measurement system is a suitable tool for measuring and improving business performance. The development in performance measures moved from financial measures focused on company profitability to measurement systems combining different methods, approaches, and tools. The paper aims to identify key performance indicators for Slovak heating companies based on the developed integrated performance measurement system. The analysis sampled 292 Slovak companies within SK NACE 35 (heating industry). The performance measurement system was built on balanced scorecard principles, while the least absolute shrinkage and selection operator (Lasso regression) method was used to select financial indicators. Based on the combination of the above methods, a performance measurement system framework for the analyzed sample of businesses was created. The results show that when managing performance, the analyzed businesses should focus on the following financial performance indicators: Receivables turnover ratio, Return on equity, Return on costs, Total debt to total assets, Material intensity, Labor to revenue ratio, Netto cash flow to assets, Net working capital to total assets, and Short-term liabilities to assets. When building performance measurement system based on balanced scorecard principles, financial indicators were supplemented by non-financial ones. In addition to the original balanced scorecard principles, the performance measurement system was extended by environmental constituents. Also, the paper’s deliverable combines Lasso regression and balanced scorecard principles in order to select key performance indices. AcknowledgmentThis paper is prepared within the grant scheme VEGA No. 1/0741/20 (the application of variant methods in detecting symptoms of possible bankruptcy of Slovak businesses in order to ensure their sustainable development)

Classifiers consensus system approach for credit scoring

Banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. Regarding this, researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Various models, from easy to advanced approaches, have been developed in this domain. However, during the last few years there has been marked attention towards development of ensemble or multiple classifier systems, which have proved their ability to be more accurate than single classifier models. However, among the multiple classifier systems models developed in the literature, there has been little consideration given to: 1) combining classifiers of different algorithms (as most have focused on building classifiers of the same algorithm); or 2) exploring different classifier output combination techniques other than the traditional ones, such as majority voting and weighted average. In this paper, the aim is to present a new combination approach based on classifier consensus to combine multiple classifier systems (MCS) of different classification algorithms. Specifically, six of the main well-known base classifiers in this domain are used, namely, logistic regression (LR), neural networks (NN), support vector machines (SVM), random forests (RF), decision trees (DT) and naïve Bayes (NB). Two benchmark classifiers are considered as a reference point for comparison with the proposed method and the other classifiers. These are used in combination with LR, which is still considered the industry-standard model for credit scoring models, and multivariate adaptive regression splines (MARS), a widely adopted technique in credit scoring studies. The experimental results, analysis and statistical tests demonstrate the ability of the proposed combination method to improve prediction performance against all base classifiers, namely, LR, MARS and seven traditional combination methods, in terms of average accuracy, area under the curve (AUC), the H-measure and Brier score (BS). The model was validated over five real-world credit scoring datasets

A comparative analysis of two-stage distress prediction models

YesOn feature selection, as one of the critical steps to develop a distress prediction model (DPM), a variety of expert systems and machine learning approaches have analytically supported developers. Data envel- opment analysis (DEA) has provided this support by estimating the novel feature of managerial efficiency, which has frequently been used in recent two-stage DPMs. As key contributions, this study extends the application of expert system in credit scoring and distress prediction through applying diverse DEA mod- els to compute corporate market efficiency in addition to the prevailing managerial efficiency, and to estimate the decomposed measure of mix efficiency and investigate its contribution compared to Pure Technical Efficiency and Scale Efficiency in the performance of DPMs. Further, this paper provides a com- prehensive comparison between two-stage DPMs through estimating a variety of DEA efficiency measures in the first stage and employing static and dynamic classifiers in the second stage. Based on experimen- tal results, guidelines are provided to help practitioners develop two-stage DPMs; to be more specific, guidelines are provided to assist with the choice of the proper DEA models to use in the first stage, and the choice of the best corporate efficiency measures and classifiers to use in the second stage

Big data techniques in auditing research and practice: current trends and future opportunities

This paper analyzes the use of big data techniques in auditing, and finds that the practice is not as widespread as it is in other related fields. We first introduce contemporary big data techniques to promote understanding of their potential application. Next, we review existing research on big data in accounting and finance. In addition to auditing, our analysis shows that existing research extends across three other genealogies: financial distress modelling, financial fraud modelling, and stock market prediction and quantitative modelling. Auditing is lagging behind the other research streams in the use of valuable big data techniques. A possible explanation is that auditors are reluctant to use techniques that are far ahead of those adopted by their clients, but we refute this argument. We call for more research and a greater alignment to practice. We also outline future opportunities for auditing in the context of real-time information and in collaborative platforms and peer-to-peer marketplaces

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress