The coronavirus crisis disrupted business survivability. Measures, like going concern opinion and bankruptcy predictors, depend on past trends extending into the future. With black swan events, past trends do not extend into the future. We propose two new metrics. The “Going Concern Survivability Index” (GCSI) is the maximum percentage revenue loss that a business can endure as a going concern. The “One Month Resilience Index” (OMRI) is the effect on the net income from the loss of the revenue for its most successful month. While OMRI is straightforward, calculating GCSI requires real options and process mining. The emerging technology of process mining and artificial intelligence are needed to capture the dynamic process by which management will juggle cash flows, sources of funds, and payment of liabilities as revenue falls. This paper is an instance of action design science research, and we discuss the steps to put our artifact into practice.
{"title":"Measuring a Business's Grit and Survivability when Faced with “Black Swan” Events Like the Coronavirus Pandemic","authors":"G. Gray, Michael G. Alles","doi":"10.2308/jeta-2020-060","DOIUrl":"https://doi.org/10.2308/jeta-2020-060","url":null,"abstract":"The coronavirus crisis disrupted business survivability. Measures, like going concern opinion and bankruptcy predictors, depend on past trends extending into the future. With black swan events, past trends do not extend into the future. We propose two new metrics. The “Going Concern Survivability Index” (GCSI) is the maximum percentage revenue loss that a business can endure as a going concern. The “One Month Resilience Index” (OMRI) is the effect on the net income from the loss of the revenue for its most successful month. While OMRI is straightforward, calculating GCSI requires real options and process mining. The emerging technology of process mining and artificial intelligence are needed to capture the dynamic process by which management will juggle cash flows, sources of funds, and payment of liabilities as revenue falls. This paper is an instance of action design science research, and we discuss the steps to put our artifact into practice.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":"1 1","pages":""},"PeriodicalIF":1.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68988605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we introduce a data analytics exercise that can be used in an intermediate financial accounting course. We discuss the concept behind the design of the exercise to allow readers to formulate similar exercises. The exercise is aimed to help students focus more on business operations, better understand business issues, consider different types of information, and use judgment to interpret findings. We also provide an assessment rubrics that can be used for similar exercises.
{"title":"Accounting Data Analytics Exercise for Intermediate Accounting: Warranty Expense and Product Liability","authors":"Ning Du, T. Wang, Ray Whittington","doi":"10.2308/jeta-2020-015","DOIUrl":"https://doi.org/10.2308/jeta-2020-015","url":null,"abstract":"In this paper, we introduce a data analytics exercise that can be used in an intermediate financial accounting course. We discuss the concept behind the design of the exercise to allow readers to formulate similar exercises. The exercise is aimed to help students focus more on business operations, better understand business issues, consider different types of information, and use judgment to interpret findings. We also provide an assessment rubrics that can be used for similar exercises.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46031538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-18DOI: 10.2308/jeta-19-10-02-40
A. Saeedi
This study compares the ability of four data mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' variousfinancial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (k-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent.
{"title":"Audit Opinion Prediction: A Comparison of Data Mining Techniques","authors":"A. Saeedi","doi":"10.2308/jeta-19-10-02-40","DOIUrl":"https://doi.org/10.2308/jeta-19-10-02-40","url":null,"abstract":"This study compares the ability of four data mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' variousfinancial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (k-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42304793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jennifer M. Cainas, Wendy M. Tietz, Tracie L. Miller-Nobles
KAT Insurance Corporation (KAT) presents two independent data analytics cases for introductory financial and managerial accounting courses, using four datasets based on anonymized real-life data (over 65,000 sales records from a national insurance company). The cases introduce students to data cleansing, data dictionaries, and data visualization topics through analysis of sales and/or cost records. The cases use Excel, Power BI, and/or Tableau for students to learn different emerging technologies and develop students' technological agility, addressing the AACSB's Accounting Accreditation Standard 5 and AICPA Accounting Competencies. Over 2,700 students have successfully completed at least one of the cases, and few students had any prior experience with Power BI and/or Tableau. Students surveyed felt their skills improved as a result of the projects, which highlights the relevance and need for this instructional resource that is designed with both accounting instructors and students in mind.
{"title":"KAT Insurance: Data Analytics Cases for Introductory Accounting Using Excel, Power BI, and/or Tableau","authors":"Jennifer M. Cainas, Wendy M. Tietz, Tracie L. Miller-Nobles","doi":"10.2308/jeta-2020-039","DOIUrl":"https://doi.org/10.2308/jeta-2020-039","url":null,"abstract":"\u0000 KAT Insurance Corporation (KAT) presents two independent data analytics cases for introductory financial and managerial accounting courses, using four datasets based on anonymized real-life data (over 65,000 sales records from a national insurance company). The cases introduce students to data cleansing, data dictionaries, and data visualization topics through analysis of sales and/or cost records. The cases use Excel, Power BI, and/or Tableau for students to learn different emerging technologies and develop students' technological agility, addressing the AACSB's Accounting Accreditation Standard 5 and AICPA Accounting Competencies. Over 2,700 students have successfully completed at least one of the cases, and few students had any prior experience with Power BI and/or Tableau. Students surveyed felt their skills improved as a result of the projects, which highlights the relevance and need for this instructional resource that is designed with both accounting instructors and students in mind.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45931574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-17DOI: 10.2308/jeta-18-12-21-26
E. Efretuei
In this study, I examine variations in the textual complexity of annual report narrative disclosures using the Fog Readability Index and Fin-Neg word list Tone Index given year and industry effects. I analyse accounting narrative Readability and Tone based on firm years, associations between the two narrative measures, and industry data. Tests of the relationship between Readability and Tone show that negative narratives have higher Readability scores, supporting the obfuscation hypothesis that bad news tends to be more difficult to read. A year analysis shows that the negative relationship between Readability and Tone increases in significance over time (2006-2011). An industry analysis shows that the observed obfuscation tends to persist in basic materials; consumer services; financial; technology; and utilities industries. This study shows that considering the effect of variations between industry and firm years can inform annual report textual complexity research and associated empirical analyses.
{"title":"Year and Industry-level Accounting Narrative Analysis: Readability and Tone Variation","authors":"E. Efretuei","doi":"10.2308/jeta-18-12-21-26","DOIUrl":"https://doi.org/10.2308/jeta-18-12-21-26","url":null,"abstract":"In this study, I examine variations in the textual complexity of annual report narrative disclosures using the Fog Readability Index and Fin-Neg word list Tone Index given year and industry effects. I analyse accounting narrative Readability and Tone based on firm years, associations between the two narrative measures, and industry data. Tests of the relationship between Readability and Tone show that negative narratives have higher Readability scores, supporting the obfuscation hypothesis that bad news tends to be more difficult to read. A year analysis shows that the negative relationship between Readability and Tone increases in significance over time (2006-2011). An industry analysis shows that the observed obfuscation tends to persist in basic materials; consumer services; financial; technology; and utilities industries. This study shows that considering the effect of variations between industry and firm years can inform annual report textual complexity research and associated empirical analyses.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46225968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is challenging for auditors to effectively and efficiently use data analytics in audit procedures and general ledger testing when the data acquired from clients is often incomplete and not in a usable format. Considerable time must be spent cleansing, transforming, standardizing, and validating the data prior to analyzing it. This problem motivated the AICPA task force to develop a set of Audit Data Standards (ADS) for streamlining the exchange of data. This paper describes an extensive exercise where students: (1) develop a Microsoft Access database that complies with the ADS for general ledger data; (2) cleanse and transform non-standardized client data for import into an ADS-compliant database; and (3) write queries for general ledger testing and journal entry testing. The exercise strengthens students' database and query-writing skills, while introducing the ADS in the context of realistic tasks to support a financial statement audit.
{"title":"Preparing for Audit Data Analytics with the AICPA General Ledger Audit Data Standards","authors":"Lorraine S. Lee, G. Casterella, Barry A. Wray","doi":"10.2308/jeta-2020-022","DOIUrl":"https://doi.org/10.2308/jeta-2020-022","url":null,"abstract":"\u0000 It is challenging for auditors to effectively and efficiently use data analytics in audit procedures and general ledger testing when the data acquired from clients is often incomplete and not in a usable format. Considerable time must be spent cleansing, transforming, standardizing, and validating the data prior to analyzing it. This problem motivated the AICPA task force to develop a set of Audit Data Standards (ADS) for streamlining the exchange of data. This paper describes an extensive exercise where students: (1) develop a Microsoft Access database that complies with the ADS for general ledger data; (2) cleanse and transform non-standardized client data for import into an ADS-compliant database; and (3) write queries for general ledger testing and journal entry testing. The exercise strengthens students' database and query-writing skills, while introducing the ADS in the context of realistic tasks to support a financial statement audit.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44437595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In light of the increased emphasis on data analytics by accounting practitioners and accreditation bodies, the objective of this paper is to present a case that will help increase students' understanding of textual analytics, which is an under-researched area of data analytics (Fisher 2018). Specifically, students will use both conditional formatting and the fuzzy lookup tool to examine a dataset for possible instances of fictitious vendor fraud, a common and often costly type of fraud. The case takes students through the comprehensive data analytics cycle. First, students are instructed how to test for fictitious vendors by using data analytic techniques. Students will then rely on the underlying data to analyze potential relationships and trends. In the final step, students will communicate results via a memorandum.
{"title":"Not So Fuzzy Auditing Analytics","authors":"Jamie Hoelscher, Trevor Shonhiwa","doi":"10.2308/jeta-2020-030","DOIUrl":"https://doi.org/10.2308/jeta-2020-030","url":null,"abstract":"\u0000 In light of the increased emphasis on data analytics by accounting practitioners and accreditation bodies, the objective of this paper is to present a case that will help increase students' understanding of textual analytics, which is an under-researched area of data analytics (Fisher 2018). Specifically, students will use both conditional formatting and the fuzzy lookup tool to examine a dataset for possible instances of fictitious vendor fraud, a common and often costly type of fraud. The case takes students through the comprehensive data analytics cycle. First, students are instructed how to test for fictitious vendors by using data analytic techniques. Students will then rely on the underlying data to analyze potential relationships and trends. In the final step, students will communicate results via a memorandum.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43346960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An M.S. in Accountancy (MSA) remains the leading graduate academic credential for students seeking to obtain employment in public accounting or corporate accounting positions. Our research corroborates the increasing demand for graduates with strong technology and analytics knowledge and skills. We advocate for an evolution from the “old” MSA model that prepares students for the CPA exam, to an “advanced” model in which the MSA essentially evolves into an MSAA (MS in Accounting Analytics). We begin with a look at the “demand” side for MSAA skills, and then describe the new MSAA degree currently offered at XXX University [1] as an example of how one university is adapting to meet this new demand. The paper concludes with advice for other programs to adapt their own MSA programs.[1] University name to be added post-review.
{"title":"The MSA is Dead. Long Live the MSA(A)!","authors":"J. Fedorowicz, Joy Gray","doi":"10.2308/jeta-2020-034","DOIUrl":"https://doi.org/10.2308/jeta-2020-034","url":null,"abstract":"An M.S. in Accountancy (MSA) remains the leading graduate academic credential for students seeking to obtain employment in public accounting or corporate accounting positions. Our research corroborates the increasing demand for graduates with strong technology and analytics knowledge and skills. We advocate for an evolution from the “old” MSA model that prepares students for the CPA exam, to an “advanced” model in which the MSA essentially evolves into an MSAA (MS in Accounting Analytics). We begin with a look at the “demand” side for MSAA skills, and then describe the new MSAA degree currently offered at XXX University [1] as an example of how one university is adapting to meet this new demand. The paper concludes with advice for other programs to adapt their own MSA programs.[1] University name to be added post-review.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48825887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the proliferation of data analytics in the field of accounting, educators need resources to enhance their curricula with analytics projects. This paper provides educators with a robust tool that generates large, unique revenue-cycle transaction data with certain realistic properties. The datasets can be used by educators to teach accounting-based data analytic procedures in accounting information systems, auditing, fraud, and data analytics classes. Additionally, multiple potential implementation opportunities for the datasets are proposed and a comprehensive example case is provided. Data Availability: The data generator can be accessed at: https://mplholt.shinyapps.io/GADGET/
{"title":"GADGET: An Accounting Data Generator","authors":"M. Holt, Bradley Lang","doi":"10.2308/jeta-2020-035","DOIUrl":"https://doi.org/10.2308/jeta-2020-035","url":null,"abstract":"\u0000 With the proliferation of data analytics in the field of accounting, educators need resources to enhance their curricula with analytics projects. This paper provides educators with a robust tool that generates large, unique revenue-cycle transaction data with certain realistic properties. The datasets can be used by educators to teach accounting-based data analytic procedures in accounting information systems, auditing, fraud, and data analytics classes. Additionally, multiple potential implementation opportunities for the datasets are proposed and a comprehensive example case is provided.\u0000 Data Availability: The data generator can be accessed at: https://mplholt.shinyapps.io/GADGET/","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47573617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Blockchain and distributed ledger technologies are changing the way financial and business records are created and stored. New approaches to collaboration within and across industries enabled by this technology will increasingly result in new opportunities for data analysis and enable fundamental changes in accounting and auditing. The importance of this technology is increasing and “CPAs will need to learn, adjust, and adapt to emerging uses of blockchain” (AICPA 2020). This paper presents a pencil-and-paper activity that can help students unfamiliar with blockchain-related technologies understand these systems, the inter-organizational databases that result from their use, and their potential impacts for the accounting profession. We include optional reference materials that can be used as background reading for faculty members unfamiliar with blockchain or that can be integrated into a course.
{"title":"Blockchain and the Future of Business Data Analytics","authors":"Stanton Heister, Matthew Kaufman, Kristi Yuthas","doi":"10.2308/jeta-2020-053","DOIUrl":"https://doi.org/10.2308/jeta-2020-053","url":null,"abstract":"\u0000 Blockchain and distributed ledger technologies are changing the way financial and business records are created and stored. New approaches to collaboration within and across industries enabled by this technology will increasingly result in new opportunities for data analysis and enable fundamental changes in accounting and auditing. The importance of this technology is increasing and “CPAs will need to learn, adjust, and adapt to emerging uses of blockchain” (AICPA 2020). This paper presents a pencil-and-paper activity that can help students unfamiliar with blockchain-related technologies understand these systems, the inter-organizational databases that result from their use, and their potential impacts for the accounting profession. We include optional reference materials that can be used as background reading for faculty members unfamiliar with blockchain or that can be integrated into a course.","PeriodicalId":45427,"journal":{"name":"Journal of Emerging Technologies in Accounting","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2020-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41758315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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