In the professional circles, factors affecting audit risk are treated independently; however, a more objective approach in assessing detection risk should be involved the relationships among the audit risk factors. This study introduces a framework based on a fuzzy multi-criteria decision support to identify the influencing factors may affect the audit risk model considering the interdependencies among them. We first takes advantages of a fuzzy screening method to screen forty more practical factors may affect the audit risk model out of 58 potential ones. Then, we applied a combined approach of Analytic Network Process (ANP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) to weight the factors and prioritize them considering their inner and outer dependencies complemented with a case study of Iran. To be more compatible with ambiguities related to human beings and make more useful decisions in the real world, the fuzzy set theory is used. The combined approach used in this study providing correct, precise weight for each factor can explore a more rigorous framework for decision-making in risk assessment by integrating interdependent relationships within and among audit risk factors. of audit evidence through acceptable audit risk, and the degrees of inherent risk and control risk . Therefore, to determine detection risk properly would influence the results of the audit.However, each component of the audit risk model is influenced by various factors. Most studies which have been conducted so far often focus only on the audit risk formula and rarely pay attention to its factors. The risk-based audit will achieve its targets if influencing factors firstly are identified.As Dusenbury et al. (2000) highlighted proper use of the audit risk model requires the component risks should be characterized as dependent risks. Anyhow, auditing standards, risk-based audit manuals, and the practical procedures individually consider audit risk components and those factors that may affect them. They treat them as independent, separate parts without taking into account any inner and outer interdependencies. Regarding the audit risk model, while the outer dependency is the one-way relationship between the underneath level that affects the upper one, the inner dependency is the relationship within factors of a certain level which are not independent of one another. The former refers to, for example, the dependency between the cluster of Inherent risk and Financial statement level factors and the latter is the dependency among factors of Financial statement level and Account the remaining sum level within Inherent risk cluster (Table 5).According to Rozario and Vasarhelyi (2018), "the external audit profession substantially lags in technological innovation."In the audit risk context, also, the traditional and normative trends mostly consider the direct and unidirectional effects of each component of the audit risk and their factors independently. Therefore, the interdependencies of them and their mutual effects are ignore...
Techniques for picking stock are of great importance in stock markets. Identifying stock picking criteria and information required by investors can help investors to make reasoned decisions and perform better than the market average. In this research, a combined approach of Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) is conducted based on Iranian brokers' expert opinions, in order to explore factors affecting investors' decisions. This method analyzes interaction levels between factors affecting stock prices in order to structure an investment model that takes these interactions into consideration. The results obtained indicate that political factors have the greatest effect on Iranian investors' decisions, followed by other investors' recommendations and then herd behavior factors. On the contrary, the use of personal and scientific analysis is uncommon.
Predicting corporate bankruptcy has been an important challenging problem in research topic in accounting and finance. In bankruptcy prediction, researchers often confront a range of observations and variables which are often vast amount of financial ratios. By reducing variables and select relevant data from a given dataset, data reduction process can optimize bankruptcy prediction. This study addresses four well-known data reduction methods including t-test, correlation analysis, principal component analysis (PCA) and factor analysis (FA) and evaluated them in bankruptcy prediction in the Tehran Stock Exchange (TSE). To this end, considering 35 financial ratios, the results of data reduction methods were separately used to train Support Vector Machine (SVM) as the powerful prediction model. Regarding the empirical results, among the aforementioned methods, the t-test lead to the most prediction rate with 97.1% of predictability and PCA by 95.1% provides the next position.
Predicting corporate bankruptcy has been an important challenging problem in research topic in accounting and finance. In bankruptcy prediction, researchers often confront a range of observations and variables which are often vast amount of financial ratios. By reducing variables and select relevant data from a given dataset, data reduction process can optimize bankruptcy prediction. This study addresses four well-known data reduction methods including t-test, correlation analysis, principal component analysis (PCA) and factor analysis (FA) and evaluated them in bankruptcy prediction in the Tehran Stock Exchange (TSE). To this end, considering 35 financial ratios, the results of data reduction methods were separately used to train Support Vector Machine (SVM) as the powerful prediction model. Regarding the empirical results, among the aforementioned methods, the t-test lead to the most prediction rate with 97.1% of predictability and PCA by 95.1% provides the next position.
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