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Argyrou, Argyris
Publications (6 of 6) Show all publications
Argyrou, A. (2013). Auditing journal entries using extreme value theory. In: ECIS 2013 - Proceedings of the 21st European Conference on Information Systems: . Paper presented at 21st European Conference on Information Systems, ECIS 2013, 5 June 2013 through 8 June 2013, Utrecht. Association for Information Systems
Open this publication in new window or tab >>Auditing journal entries using extreme value theory
2013 (English)In: ECIS 2013 - Proceedings of the 21st European Conference on Information Systems, Association for Information Systems, 2013Conference paper, Published paper (Refereed)
Abstract [en]

While a wealth of statutory and auditing pronouncements attest to the importance of the auditing of journal entries for preventing and detecting material misstatements to financial statements, existing literature has so far paid inadequate attention to this line of research. To explore this line of research further, this paper proposes a bipartite model that is based on extreme value theory and Bayesian analysis of Poisson distributions. The paper assesses the veracity of the model via a series of experiments on a dataset that contains the journal entries of an international shipping company for fiscal years 2006 and 2007. Empirical results suggest the model can detect journal entries that have a low probability of occurring and a monetary amount large enough to cause financial statements to be materially misstated. Further investigations reveal that the model can assist auditors to form expectations about the journal entries thus detected as well as update their expectations based on new data. The findings indicate that the model can be applied for the auditing of journal entries, and thus supplement existing procedures.

Place, publisher, year, edition, pages
Association for Information Systems, 2013
Keywords
Auditing, Bayesian analysis, Extreme value theory, Journal entries, Hardware, Financial statements, Fiscal years, International shippings, Low probability, Information systems
National Category
Computer Sciences Business Administration
Identifiers
urn:nbn:se:hj:diva-39023 (URN)2-s2.0-84905842124 (Scopus ID)
Conference
21st European Conference on Information Systems, ECIS 2013, 5 June 2013 through 8 June 2013, Utrecht
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved
Argyrou, A. (2012). Auditing journal entries using self-organizing map. In: 18th Americas Conference on Information Systems 2012, AMCIS 2012: . Paper presented at 18th Americas Conference on Information Systems 2012, AMCIS 2012, 9 August 2012 through 12 August 2012, Seattle, WA, USA (pp. 986-995).
Open this publication in new window or tab >>Auditing journal entries using self-organizing map
2012 (English)In: 18th Americas Conference on Information Systems 2012, AMCIS 2012, 2012, p. 986-995Conference paper, Published paper (Refereed)
Abstract [en]

A considerable body of regulatory pronouncements attests to the significance of auditing journal entries for ensuring that financial statements are free of material misstatements; however, existing empirical studies have paid insufficient attention to the audit of journal entries. To explore this issue further, this paper proposes a model based on self-organizing map as well as validates this model by performing experiments on a dataset containing journal entries. Empirical results suggest that the proposed model can detect "suspicious" and legitimate transactions with a high degree of accuracy. Further investigations reveal that the performance of the model is robust to varying prior probabilities of "suspicious" journal entries occurring in the population. The findings indicate that the model can assist auditors in detecting "suspicious" journal entries.

Keywords
Auditing, Journal entries, Self-organizing map, Empirical studies, Financial statements, High degree of accuracy, Model-based OPC, Prior probability, Conformal mapping, Information systems, Management
National Category
Business Administration Information Systems
Identifiers
urn:nbn:se:hj:diva-39024 (URN)2-s2.0-84877880685 (Scopus ID)9781622768271 (ISBN)
Conference
18th Americas Conference on Information Systems 2012, AMCIS 2012, 9 August 2012 through 12 August 2012, Seattle, WA, USA
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved
Argyrou, A. (2012). Investigating financial distress: The case of macroeconomic uncertainty. In: : . Paper presented at 35th Annual Congress of the European Accounting Association, Ljubljana, Slovenia, May 5-8, 2012 (Research Forum: Accounting Information Systems).
Open this publication in new window or tab >>Investigating financial distress: The case of macroeconomic uncertainty
2012 (English)Conference paper, Oral presentation only (Other academic)
National Category
Business Administration Information Systems
Identifiers
urn:nbn:se:hj:diva-39030 (URN)
Conference
35th Annual Congress of the European Accounting Association, Ljubljana, Slovenia, May 5-8, 2012 (Research Forum: Accounting Information Systems)
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved
Andreev, A. & Argyrou, A. (2012). Using self-organizing map for data mining: A synthesis with accounting applications. In: Dawn E. Holmes & Lakhmi C. Jain (Ed.), Data mining: Foundations and intelligent paradigms: Volume 3: Medical, health, social, biological and other applications (pp. 321-342). Berlin: Springer
Open this publication in new window or tab >>Using self-organizing map for data mining: A synthesis with accounting applications
2012 (English)In: Data mining: Foundations and intelligent paradigms: Volume 3: Medical, health, social, biological and other applications / [ed] Dawn E. Holmes & Lakhmi C. Jain, Berlin: Springer, 2012, p. 321-342Chapter in book (Refereed)
Abstract [en]

The self-organizing map (i.e. SOM) has inspired a voluminous body of literature in a number of diverse research domains. We present a synthesis of the pertinent literature as well as demonstrate, via a case study, how SOM can be applied in clustering accounting databases. The synthesis explicates SOM's theoretical foundations, presents metrics for evaluating its performance, explains the main extensions of SOM, and discusses its main financial applications. The case study illustrates how SOM can identify interesting and meaningful clusters that may exist in accounting databases. The paper extends the relevant literature in that it synthesises and clarifies the salient features of a research area that intersects the domains of SOM, data mining, and accounting. 

Place, publisher, year, edition, pages
Berlin: Springer, 2012
Series
Intelligent Systems Reference Library, ISSN 1868-4394 ; 25
National Category
Business Administration Information Systems
Identifiers
urn:nbn:se:hj:diva-39025 (URN)10.1007/978-3-642-23151-3_14 (DOI)2-s2.0-84885580042 (Scopus ID)9783642231506 (ISBN)
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved
Argyrou, A. & Andreev, A. (2011). A semi-supervised tool for clustering accounting databases with applications to internal controls. Expert systems with applications, 38(9), 11176-11181
Open this publication in new window or tab >>A semi-supervised tool for clustering accounting databases with applications to internal controls
2011 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 38, no 9, p. 11176-11181Article in journal (Refereed) Published
Abstract [en]

A considerable body of literature attests to the significance of internal controls; however, little is known on how the clustering of accounting databases can function as an internal control procedure. To explore this issue further, this paper puts forward a semi-supervised tool that is based on self-organizing map and the IASB XBRL Taxonomy. The paper validates the proposed tool via a series of experiments on an accounting database provided by a shipping company. Empirical results suggest the tool can cluster accounting databases in homogeneous and well-separated clusters that can be interpreted within an accounting context. Further investigations reveal that the tool can compress a large number of similar transactions, and also provide information comparable to that of financial statements. The findings demonstrate that the tool can be applied to verify the processing of accounting transactions as well as to assess the accuracy of financial statements, and thus supplement internal controls.

Place, publisher, year, edition, pages
Elsevier, 2011
Keywords
Clustering accounting databases, Internal controls, Self-organizing map, Empirical results, Financial statements, Self organizing, Semi-supervised, Shipping companies, Conformal mapping, Database systems, Finance, Equipment
National Category
Business Administration Information Systems
Identifiers
urn:nbn:se:hj:diva-39026 (URN)10.1016/j.eswa.2011.02.163 (DOI)000291118500050 ()2-s2.0-79955606937 (Scopus ID)
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved
Argyrou, A. (2009). Clustering hierarchical data using self-organizing map: A graph-theoretical approach. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): . Paper presented at 7th International Workshop on Self-Organizing Maps, WSOM 2009; St. Augustine, FL; United States; 8 June 2009 through 10 June 2009; Code 77058 (pp. 19-27). Springer, 5629
Open this publication in new window or tab >>Clustering hierarchical data using self-organizing map: A graph-theoretical approach
2009 (English)In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2009, Vol. 5629, p. 19-27Conference paper, Published paper (Refereed)
Abstract [en]

The application of Self-Organizing Map (SOM) to hierarchical data remains an open issue, because such data lack inherent quantitative information. Past studies have suggested binary encoding and Generalizing SOM as techniques that transform hierarchical data into numerical attributes. Based on graph theory, this paper puts forward a novel approach that processes hierarchical data into a numerical representation for SOM-based clustering. The paper validates the proposed graph-theoretical approach via complexity theory and experiments on real-life data. The results suggest that the graph-theoretical approach has lower algorithmic complexity than Generalizing SOM, and can yield SOM having significantly higher cluster validity than binary encoding does. Thus, the graph-theoretical approach can form a data-preprocessing step that extends SOM to the domain of hierarchical data.

Place, publisher, year, edition, pages
Springer, 2009
Keywords
Clustering, Graph theory, Hierarchical data, SOM, Algorithmic complexity, Binary encodings, Cluster validity, Complexity theory, Graph theoretical approach, Numerical attributes, Numerical representation, Pre-processing step, Quantitative information, Real life data, Selforganizing map, Computational complexity, Conformal mapping, Electric converters, Encoding (symbols), Parallel processing systems, Self organizing maps, Strength of materials
National Category
Business Administration Information Systems
Identifiers
urn:nbn:se:hj:diva-39027 (URN)10.1007/978-3-642-02397-2_3 (DOI)2-s2.0-69049117373 (Scopus ID)3642023967 (ISBN)9783642023965 (ISBN)
Conference
7th International Workshop on Self-Organizing Maps, WSOM 2009; St. Augustine, FL; United States; 8 June 2009 through 10 June 2009; Code 77058
Available from: 2018-03-20 Created: 2018-03-20 Last updated: 2018-03-20Bibliographically approved

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