The Role of Data Analytics and Machine Learning in Resurrecting Inductive-Based Accounting Research
DOI:
https://doi.org/10.14738/tmlai.92.9823Abstract
The objective of this paper is to assess the impact of data analytics (DA) and machine learning (ML) on accounting research.[1] As discussed in the paper, the inherent inductive nature of DA and ML is creating an important trend in the way accounting research is being conducted. That trend is the increasing utilization of inductive-based research among accounting researchers. Indeed, as a result of the recent developments with DA and ML, a rebalancing is taking place between inductive-based and deductive-based research in accounting.[2] In essence, we are witnessing the resurrection of inductive-based accounting research. A brief review of some empirical evidence to support the above argument is also provided in the paper.
References
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(48) Sun, T., “Applying deep learning to audit procedures: An illustrative framework,” Accounting Horizons, Vol. 33, No. 3 (2019), pp. 89–109.
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(2) Allodi, L., and F. Massacci (2017). Security Events and Vulnerability Data for Cybersecurity Risk Estimation. Risk Analysis. 37 (8): 1606-1627.
(3) Amel-Zadeh, A., J-P Calliess, D. Kaiser, and S. Roberts (2020). “Machine Learning-Based Financial Statement Analysis,” SSRN Working Paper (see: https://ssrn.com/abstract=3520684).
(4) Appelbaum, D., A. Kogan, M. Vasarhelyi, and Z. Yan (2017). “Impact of business analytics and enterprise systems on managerial accounting,” International Journal of Accounting and Information Systems, Vol. 25, pp. 29-44.
(5) Askary, S., N. Abu-Ghazaleh, Y. A. Tahat (2018). “Artificial Intelligence and Reliability of Accounting Information,” in Al-Sharhan S. et al. (eds) Challenges and Opportunities in the Digital Era. I3E 2018. Lecture Notes in Computer Science, vol 11195. Springer, Cham (see: https://doi.org/10.1007/978-3-030-02131-3_28).
(6) Athey, S. (May 2019). “The Impact of Machine Learning on Economics,” The Economics of Artificial Intelligence: An Agenda. Editors: A. Agrawal, J. Gans, and A. Goldfarb, University of Chicago Press: 507-547. Available at: file:///C:/Users/lgordon/AppData/Local/Temp/c14009-1.pdf.
(7) Barboza, F., H. Kimura, and E. Altman (2017). “Machine learning models and bankruptcy prediction,” Expert Systems with Applications, 83, pp. 405–417.
(8) Berle, A. and G. Means (1932). The Modern Corporation and Private Property. New York: Macmillan Company.
(9) Bertomeu, J., E. Cheynel, E. Floyd, and W. Pan, “Using machine learning to detect misstatements, Review of Accounting Studies, forthcoming.
(10) Bao, Y., B. Ke, B. Li, Y. J. Yu and J. Zhang (2020). “Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach,” Journal of Accounting Research, Vol. 58, No. 1, pp. 199-235.
(11) Bhimani, A. (2020). “Digital data and management accounting: why we need to rethink research methods,” Journal of Management Control, Vol. 31, Nos. 1-2, pp. 9–23 (see: https://doi.org/10.1007/s00187-020-00295-z).
(12) Bhimani, A. and L. Willcocks (2014). “P.D. Lecture: Digitisation, ‘Big Data’ and the transformation of accounting information,” Accounting and Business Research, Vol. 44, No. 4 (2014), pp. 469-490.
(13) Brown, N. C., R. M. Crowley and W. B. Elliott (2020). “What are you saying? Using topic to detect financial misreporting,” Journal of Accounting Research, 58 (1): 237–291.
(14) Cheong, A., S. Cho, W. G. No, and M. Vasarhelyi (2019). “If You Cannot Measure It, You Cannot Manage It: Assessing the Quality of Cybersecurity Risk Disclosure through Textual Imagification,”Available at SSRN: https://ssrn.com/abstract=3474575).
(15) Cho, S., M. Vasarhelyi, T. Sun, and C. Zhang (2020). “Learning from Machine Learning in Accounting and Assurance,” Journal of Emerging Technologies in Accounting, Vol. 17, No. 1, pp. 1–10.
(16) Glen, D. A., J. B. Hill, and M. Wetklow (2018-2019). “The ABCDs Of Technology and Impacts On Federal Financial Management: A CFO Community Perspective,” Journal of Government Financial Management, Winter, pp. 43-47.
(17) Gordon, L. A. (2004). Managerial Accounting: Concepts and Empirical Evidence, McGraw-Hill, Custom Publishing, New York.
(18) Gordon, L. A. [2018]. “The Impact of Technology on Contemporary Accounting: An ABCD Perspective,” Transactions on Machine Learning and Artificial Intelligence, Vol. 6, No. 5, pp. 10-16.
(19) Gordon, L. A. and M. P. Loeb (2002). “The Economics of Information Security Investment,” ACM Transactions on Information and System Security, Vol. 5, No. 4, pp. 438-457.
(20) Gordon, L. A., M. P. Loeb, and L. Zhou (2011). “The Impact of Information Security Breaches: Has there been a Downward Shift?,” Journal of Computer Security, Vol. 19, No. 1, 2011, pp. 33-56.
(21) Gordon, L. A., M. P. Loeb, and L. Zhou (2016). “Investing in Cybersecurity: Insights from the Gordon-Loeb Model,” Journal of Information Security, Vol. 7, pp. 49-59.
(22) Hsieh, T-S., J-B. Kim, R. Wang, and Z. Wang (2020). “Seeing is believing? Executives’ facial trustworthiness, auditor tenure, and audit fees,” Journal of Accounting and Economics, 2020, forthcoming (available at: https://doi.org/10.1016/j.jacceco.2019.101260).
(23) Huerta, E. and S. Jensen (2017). “An Accounting Information Perspective on Data Analytics and Big Data,” Journal of Information Systems, Vol. 31, No. 3, pp. 101-114.
(24) Hunt, J. O., J. N. Myers, and L. A. Myers, L. A. (2019). “Improving Earnings Predictions with Machine Learning,” working paper (available at: https://zicklin.baruch.cuny.edu/wp-content/uploads/sites/10/2019/12/Improving-Earnings-Predictions-with-Machine-Learning-Hunt-Myers-Myers.pdf).
(25) Hunt, J. O., D. M. Rosser and S. P. Rowe (2020). “Using machine learning to predict auditor switches: How the likelihood of switching affects audit quality among non-switching clients,” Journal of Accounting and Public Policy, in press.
(26) Ijiri, Yuji (1967): The Foundations of Accounting Measures, Englewood Cliffs, NJ: Prentice-Hall, Inc.
Johnson, R. A. and D. W. Wichern (2019). Applied Multivariate Statistical Analysis (Classic Version), 6th Edition, Pearson.
(27) Kleinberg, J., H. Lakkaraju, J. Leskovec, J. Ludwig, S. Mullainathan. 2018. “Human Decisions and Machine Predictions,” Quarterly Journal of Economics, 133(1) :237-293.
(28) Lahann, L., M. Scheid and P. Fettke (2019). "Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data," 2019 IEEE 21st Conference on Business Informatics (CBI), Moscow, Russia, pp. 1-10 (available at: doi: 10.1109/CBI.2019.00008).
(29) Leo, S. S. and K. Maddulety (2019). “Machine Learning in Banking Risk Management: A Literature Review,” MDPI, Basel, Switzerland, March (available at: file:///C:/Users/lgordon/AppData/Local/Temp/risks-07-00029-v2.pdf).
(30) Lily Shen, Stephen Ross. 2021. Information value of property description: A Machine learning approach. Journal of Urban Economics, 121, 103299.
(31) Lokanan, L., V. Tran and N. H. Vuong (2019). “Detecting anomalies in financial statements using machine learning algorithm: The case of Vietnamese listed firms,” Asian Journal of Accounting Research, Vol. 4 No. 2, pp. pp. 181-201.
(32) Master’s in Data Science, “What is Data Analytics,” (see: https://www.mastersindatascience.org/resources/what-is-data-analytics/).
(33) Mattessich, R. (2005). “A concise history of analytical accounting: examining the use of mathematical notions in our discipline,” Spanish Journal of Accounting History, No. 2 (June), pp. 123-153 (available at: file:///C:/Users/lgordon/AppData/Local/Temp/119f40826607ea7fc9d761af53a27b7c6a33.pdf).
(34) Mirzaey, M., M. Jamshidi, and Y. Hojatpour (2017). “Applications of Artificial Neural Networks in Information System of Management Accounting,” The International Journal of Mechatronics, Electrical and Computer Technology (IJMECT), Vol. 7, No. 25, pp. 3523-3530 (available at: file:///C:/Users/lgordon/AppData/Local/Temp/Vol7_Iss25_3523-3530_Applications_of_Artificial_Neural_N.pdf).
(35) Mullainathan, S. and J. Spiess (2017). “Machine Learning: An Applied Econometric Approach,” Journal of Economic Perspective, Vol. 31, No. 2, pp. 87–106
(36) Nielsen, S. (2020). "Management accounting and the idea of machine learning," Economics Working Papers 2020-09, Department of Economics and Business Economics, Aarhus University (available at: file:///C:/Users/lgordon/AppData/Local/Temp/wp20_09.pdf).
(37) No, W. G., K. Lee, F. Huang and Q. Li, “Multidimensional Audit Data Selection (MADS): A Framework for Using Data Analytics in the Audit Data Selection Process,” Accounting Horizons, Vol. 33, No. 3 (2019), pp. 127-140.
(38) Paton, W. A., and A. C. Littleton, “An Introduction to Corporate Accounting Standards. Monograph No. 3, Chicago, IL: American Accounting Association, 1940.
(39) Perols, J. L., R. M. Bowen, C. Zimmermann, and B. Samba, B. (2017), “Finding needles in a haystack: Using data analytics to improve fraud prediction,” The Accounting Review, Vol. 92, No. 2, pp. 221–245.
(40) Peters, G., P. V. Shevchenko, R. Cohen, and D. Maurice (2017). “Statistical Machine Learning Analysis of Cyber Risk Data: Event Case Studies (November 18). (available at: https://ssrn.com/abstract=3073704 or http://dx.doi.org/10.2139/ssrn.3073704).
(41) Rafiei, M., and H. Adeli, “Novel Machine-Learning Model for Estimating Construction Costs Considering Economic Variables and Indexes,” Journal of Construction Engineering and Management, Vol. 144, No. 12 (2018), see: file:///C:/Users/lgordon/AppData/Local/Temp/Novel_Machine_Learning_Model_for_Estimat-1.pdf.
(42) Rawat, D. B., R. Doku, and M. Garuba (2019). “Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security,” IEEE Transactions on Services Computing, March (available at: file:///C:/Users/lgordon/AppData/Local/Temp/10094358.pdf).
(43) Richardson, V., K. Terrell, R. Teeter, Data Analytics for Accounting, 2nd Ed., McGraw-Hill, Inc., 2021.
(44) Shimamoto, D. C., “Why Accountants Must Embrace Machine Learning,” International Federation of Accountants, April 18, 2018 (see: https://www.ifac.org/knowledge-gateway/preparing-future-ready-professionals/discussion/why-accountants-must-embrace-machine-learning).
(45) Silver, D., T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. Lillicrap, K. Simonyan, D. Hassabis (2018). “A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play,” Science December 7, Vol. 362, Issue 6419, pp. 1140-1144.
(46) Spanos, G. and L. Angelis (2016). “The impact of information security events to the stock market: A systematic literature review.” Computers & Security, 58, pp, 216-229.
(47) Spiceworks, P. Future Tech: Storage capacity in the year 2030 and beyond, June 15, 2017 (see: https://community.spiceworks.com/topic/1993877-future-tech-storage-capacity-in-the-year-2030-and-beyond).
(48) Sun, T., “Applying deep learning to audit procedures: An illustrative framework,” Accounting Horizons, Vol. 33, No. 3 (2019), pp. 89–109.
(49) Tolubko, V., V. Vyshnivskyi, V. Mukhin, H. Haidur, N. Dovzhenko, O. Ilin, V. Vasylenko (2018). “Method for Determination of Cyber Threats Based on Machine Learning for Real-Time Information System, International Journal of Intelligent Systems and Applications,” Vol. 8, pp. 11-18 (available at: file:///C:/Users/lgordon/AppData/Local/Temp/IJISA-V10-N8-2-1.pdf).
(50) U.S. Securities and Exchange Commission (SEC, 2011). “Disclosure Guidance on Cybersecurity Risks and Cyber Incidences” CF Disclosure Guidance: Topic No. 2,” (see: http://www.sec.gov/divisions/corpfin/guidance/cfguidance-topic2.htm).
(51) U.S. Securities and Exchange Commission (SEC, 2018), “Commission Statement and Guidance on Public Company Cybersecurity Disclosures” (see: https://www.sec.gov/news/press-release/2018-22).
(52) Varian, H. R. (2014). “Big Data: New Tricks for Econometrics,” Journal of Economic Perspective, Vol. 28, No. 2, pp. 3–28.
(53) [53] Vasarhelyi, M. A., A. Kogan, and B. M. Tuttle (2015). “Big Data in Accounting: An Overview,” Accounting Horizons Vol. 29, No. 2, pp. 381-396.
(54) Wei, R., B. Dong, Q. Zheng, X. Zhu, J. Ruan and H. He, "Unsupervised Conditional Adversarial Networks for Tax Evasion Detection," IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 1675-1680 (doi: 10.1109/BigData47090.2019.9005656).
(55) Zalaghi, H. and M. Khazaei, “The Role of Deductive Reasoning in Accounting Research and Standard Setting,” Asian Journal of Finance & Accounting, Vol. 8, No.1 (2016), pp. 227-241.
(56) Zeff, S. A. (2018). “An Introduction to Corporate Accounting Standards: Detecting Paton’s and Littleton’s Influences,” Accounting Historians Journal, Vol. 45, No. 1, pp. 45–67.
(57) Zeff, S. A. (2019). “A Personal View of the Evolution of the Accounting Professoriate,” Accounting Perspectives, Vol. 18, No. 3, pp. 159-185.
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2021-04-01
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Gordon, L. A. . (2021). The Role of Data Analytics and Machine Learning in Resurrecting Inductive-Based Accounting Research. Transactions on Engineering and Computing Sciences, 9(2), 1–19. https://doi.org/10.14738/tmlai.92.9823
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