Measuring Accruals Through Textual Reports Using Support Vector Regression Technique and Dictionary-based Approach
Subject Areas : Management Accounting
Zohreh Zivdar
1
(Ph.D Student of Accounting, Department of Accounting, Shahrekord Branch,Islamic Azad University, Shahrekord, Iran.)
Dariush Foroughi
2
(Associate Professor of Accounting, University of Isfahan, Isfahan, Iran.)
Gholamhosein Kiani
3
(Assistant Professor of Economy, University of Isfahan, Isfahan, Iran.)
Keywords: accruals, Unstructured Data, Text mining,
Abstract :
The purpose of this study is to measure and estimate accruals of listed companies in Tehran Stock Exchange through qualitative and unstructured reports using text mining techniques. The study sample included 205 companies in the period 2011-2017. The statistical technique used to test the hypotheses is multivariate regression of the panel data type using stata software Textual accruals were measured by the board of directors' report to the corporation using two support vector regression and dictionary-based methods in Python software.The findings showed that 19.6% of firm-level accruals changes (actual accruals) can be explained by textual accruals. The results also indicate that dictionary accruals (estimated on the basis of dictionary) are able to explain15/3% of the changes are actual accruals, thus textual accruals have more interpretative power than dictionary accruals to explain and describe actual accruals in listed companies in Tehran Stock Exchange. Textual and vocabulary accruals can be used to explain 53% of actual accrual variations if they are used simultaneously in a model to describe actual accruals.
* اسماعیلی، مهدی(1391). داده کاوی مفاهیم و تکنیکها. انتشارات: نیاز دانش، چاپ سوم، ص38
* افلاطونی، ع. (1395). تحلیلآماری در پژوهشهای مالی و حسابداری بانرم افزار استتا. انتشارات ترمه. چاپ اول.
* بنی مهد، ب.، م.عربی، ش.حسنپور. (1396). پژوهشهای تجربی و روش شناسیدرحسابداری. انتشارات ترمه، چاپ سوم.
* ثقفی،ع. (1393). نظریه های حسابداری. نشر: انجمن حسابداری ایران با همکاری انتشارات ترمه. جلد اول. چاپ سوم.ص143 .
* دارابی، ر.وغ. مشایخی. (1395). تاثیر هوش مالی در پیشبینی ریسک اعتباری با استفاده از مدل ماشین بردار پشتیبان.تحقیقات حسابداری و حسابرسی(30):102-125
* رحیمی دستجردی، م، ا.خدامی پور، وم. بهار مقدم. (1395). بررسی قیمت گذاری اقلام عادی و غیر عادی اجزای نقدی و تعهدی سود. فصلنامه حسابداری مالی ۸ (۳۱) :۱61-۱33
* رهروی دستجردی، ع. (1396). برآورد میزان احتمال خطر تقلب در گزارشگری مالی از طریق تجزیه و تحلیل متن و تطبیق انگیزه های آن با الگوی نظریه چشم انداز تجمعی. رساله جهت اخذ درجه دکترای تخصصی حسابداری، دانشگاه اصفهان.
* ظفری،س؛د.فروغی؛غ.کیانی.(1398).تاثیرقابلیت مقایسه وثبات رویه برعدم تقارن اطلاعاتی: رویکرد متن کاوی، نشریه علمی حسابداری مدیریت، سال12، شماره41، 133-150
* فروغی، د.،ه. امیری، وه. شیخی. (1392). تاثیر کیفیت اقلام تعهدی بر صرف ریسک شرکت های پذیرفته شده در بورس اوراق بهادار تهران.پژوهش های حسابداری مالی 5 (1): 13-28.
* نوروش، ا، ور. شیروانی. (1394). فرهنگ حسابداری نوروش: اصطلاحات حسابداری ، مالی و مدیریت، چاپ هشتم، انتشارات اشراقی.
* Ajina,A., M Laouiti, B Msolli.(2016). Guiding through the Fog: Does annual report readability reveal earnings management? Research in International Business and Finance.38, 509-516.
* Allee,K.,DeAngelis,M.,(2015).The structure of voluntary disclosure narratives : evidence from tone dispersion. Journal of Accounting Research,53(2),241–274.
* Amani,F.A.,&Fadlalla,A.M.(2017). Data mining applications in accounting: A review of the literature and organizing framework. International Journal of Accounting Information Systems,24,32-58.
* Aureli,Selena.(2017).A comparison of content analysis usage and text mining in corporate disclosure.The International Journal of Digital Accounting Research,17:1-32.
* Ball, C., Hoberg, G., Maksimovic, V., (2015). Disclosure, Business Change, and Earnings Quality .Available at SSRN.
* Barth ME, Cram D, Nelson K (2001). Accruals and the prediction of future cash flows. The Accounting Review, 76(January):27–58.
* Beattie, V. (2014). Accounting narratives and the narrative turn in accounting research: issues, theory, methodology, methods and a research framework. British Accounting Review, 46(2), 111-134
* Chen, J. V., and F. Li.(2017). Estimating the Amount of Estimation in Accruals. Available at SSRN: https://ssrn.com/abstract=2738842
* Chen, Y.; Lingling, Zh.; and L. Zhang (2013). “Financial Distress Prediction for Chinese Listed Manufacturing Companies”, Procedia Computer Science. Vol. 17, pp. 678-686.
* Chou,C.Janie Chang,Chen,Cand W.Chiang(2018)MeasuringtheConsistency of Quantitative and Qualitative Information in Financial Reports: A Design Science Approach. Journal of Emerging Technologies in Accounting , Vol. 15, No. 2, pp. 93-109.
* Dechow, P.M., W. Ge, and K.M. Schrand (2010): Understanding earnings quality: A review of the proxies, their determinants and their consequences, Journal of Accounting and Economics ,50: 344-401.
* Dechow, P. M., and I. D. Dichev. (2002). The quality of accruals and earnings: role of accrual estimation errors. The Accounting Review ,77: 5–35
* Drew,J., K,Tysiac.(2019). What to expect in 2020? Journal of Accountancy., December 1.
* Dyer, T., Lang M., and Stice-Lawrence, L.(2016). The Ever-Expanding 10-K: Why are 10-Ks Getting so Much Longer (and Does it Matter)? Working paper.
* Frankel,R.,Jennings.,J and J.Lee.(2016).Using Unstructured and Qualitative Disclosure to Explain Accruals. Journal of Accounting and Economics, 62, ( 2–3):209-227
* Han, I., Kim, B. Y., Lee, J., & Park, S. H.(2013). “Information Asymmetry and the Accrual Anomaly”. KAIST College of Business Working Paper Series, (2013034)
* Healy, P. & Wahlen, J. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons,13 (4): 365-383.
* Hribar,P. Samuel J. Melessa, R. Christopher Small and Jaron H. Wilde.(2017). Does Managerial Sentiment Affect Accrual Estimates? Evidence from the Banking Industry, Journal of Accounting and Economics,63(1): 26-50
* Lang, M., and Stice-Lawrence, L., (2015). Textual Analysis and International Financial Reporting: Large Sample Evidence, Journal ofAccounting and Economics,110–135
* Leuz, C., and, P. Wysocki. (2016). The Economics of Disclosure and Financial Reporting Regulation:Evidence and Suggestions for Future Research. Journal of Accounting Research, 54:525–622.
* Li F.(2008). Annual report readability, current earnings, and earnings persistence, Journal of Accounting and Economics, 45(2):221-247.
* Li Guo, Feng Shi, Jun Tu.,(2016). Textual analysis and machine leaning: Crack unstructured data in finance and accounting. The Journal of Finance and Data Science, 2 : 153-170
* Lo.,K.,Ramos.,F. and R.Rogo.,(2017). Earnings management and annual report readability,Journal of Accounting and Economics, (in Press).
* Loughran, T., McDonald, B., 2014. Measuring readability in financial disclosures. The Journal of Financ. 69, 1643–1671.
* Mayew.W.J., Sethuraman,M and M. Venkatachalam.(2015). MD&A Disclosure and the Firm's Ability to Continue as a Going Concern, The Accounting Review90(4): 1621-1651
* Muslu,V.,Radhakrishnan,S.,Subramanyam,K.R.,Lim,D.,(2014).Forward-lookingMD&A disclosures and the information environment.,Management Science ,61(5):931–948.
* Sloan, R., (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review,71:289–316.
* Tetlock PC.(2007). Giving content to investor sentiment: the role of media in the stock market. Journal of Finance,62(3):1139-1168.
* Turegun,N.,(2019). Text Mining in Financial Information, Current Analysis on Economics & Finance,Vol 1:18-26
* Vapnik,V.,(1998), Statistical Learning Theory.Wiley, New York
* Data mining: Practical machine learning tools and techniques. Morgan Kaufmann.
* Wooldridge, J. M. (2001). "Applications of Generalized Method of Moments Estimation. Journal of Economic Perspectives,15(4)
* Zhang, Y.( 2012 ). "The empirical study of earnings management based on Chinese listed companies," Lingnan Journal of Banking, Finance and Economics: Vol. 3, Article 2
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