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Predicting Going Concern of Companies Using the Tone of Auditor Reporting | ||
| Journal of Mathematics and Modeling in Finance | ||
| دوره 2، شماره 2، اسفند 2022، صفحه 181-194 اصل مقاله (211.96 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2023.15192 | ||
| نویسندگان | ||
| Hamid Abbaskhani1؛ Asgar Pakmaram* 2؛ Nader Rezaei2؛ Jamal Bahri Sales3 | ||
| 1Department of accounting | ||
| 2Department of Accounting, Bonab Branch, Islamic Azad University, Bonab, Iran | ||
| 3Department of Accounting, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| چکیده | ||
| Despite the growing need for research on the going concern and bankruptcy of companies, most of the conducted studies have used the approach of quantitative data for predicting the going concern and bankruptcy of companies; on the other hand, it is possible to manage these quantitative data by company managers. As a result, there appears to be a need to examine alternative methods for predicting going concern and bankruptcy based on qualitative data from the auditor's report. The purpose of this research is to determine the ability to predict the going concern of the companies using quantitative and qualitative data. The study period was from 2011 to 2021, with a sample of 54 companies admitted to the Tehran Stock Exchange. The results of the first hypothesis test show that the coefficient of determination of text-mining approach model prediction with the presence of a life cycle variable is greater than the determination coefficient of text-mining approach model prediction with the presence of a company size variable. The test of the second hypothesis shows that the difference in the increasing explanatory power of the first model compared to the second model in the companies accepted in the stock exchange is significant. | ||
| کلیدواژهها | ||
| Financial Forecasting؛ Going Concern؛ Tone Analysis؛ Auditor Reporting | ||
| مراجع | ||
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[1] Altman, E.I. , 1968, Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy, Journal of Finance, 23 , 589-609. [2] Altman, E.I., Sabato, G., & Wilson, N., 2010, The value of non- nancial information in small and medium-sized enterprise risk management, Journal of Credit Risk, 2 , 95-127. [3] Banimahd, B., Arabi, M. & Hasanpor, Sh., 2016, Experimental research and methodology in accounting, Termeh Publications. [4] Campbell, J. Y., Hilscher, J., & Szilagyi, J. , 2008, In search of distress risk, The Journal of Finance, 63 , 2899-2939. [5] Davis, A. K., Ge, W., Matsumoto, D., & Zhang, J. L., 2015, The effect of managerspeci c optimism on the tone of earnings conference calls, Review of Accounting Studies,20(2), 639673. [6] Drass, K. A. , 2019, Text Analysis and Text-Analysis Software: A Comparison of Assumptions, New Technology in Sociology, , 155162. [7] Elsayed, M., Elsayed, M., Elshandidy, T., & Elshandidy, T. , 2020, Do narrative-related disclosures predict corporate failure? Evidence from UK non- nancial publicly quoted rms, International Review of Financial Analysis, 71, 101555. [8] Feldman, R., Govindaraj, S., Livnat, J., & Segal, B. , 2010, Management's tone change, post earnings announcement drift and accruals, Review of Accounting Studies, 15(4), 915953. [9] Garcia, D., 2013, Sentiment during recessions, The Journal of Finance, 68(3), 12671300. [10] Grunert, J.P., Norden, L., Weber, M., & Weber, M., 2005, The role of non- nancial factors in internal credit ratings, Journal of Banking and Finance, 29, 509-531. [11] Hamer, M.M. , 1983, Failure prediction: sensitivity of classi cation accuracy to alternative statistical methods and variable sets, Journal of Accounting and Public policy, 2, 289-307. [12] Henry, E. , 2008, Are investors in uenced by how earnings press releases are written?, The Journal of Business Communication(1973), 45(4), 363-407. [13] Hobbs, J. R., Walker, D. E., & Amsler, R. A. , 1982, Natural language access to structured text, Proceedings of the ninth Conference on Computational Linguistics. [14] Hotho, A., Nurnberger, A., & Paass, G. , 2005, A brief survey of text mining, LDV Forum, 20, 19-62. [15] Huang, X., Teoh, S. H., & Zhang, Y. , 2013, Tone management, The Accounting Review,89(3), 10831113. [16] Jo, N., & Shin, K. , 2016, Bankruptcy prediction modeling using qualitative information based on big data analytics. [17] Kashanipoor, M., Aghaee, M.A., & Mohseni Namaghi, D. , 2020, Information Disclosure Tone and Future Performance, Accounting and Auditing Review, 26(4), 570-594. (In Persian) [18] Kou, J. , 2022, Analysing Housing Price in Australia with Data Science Methods, Doctoral dissertation, Victoria University. [19] Kumar, L., & Bhatia, P. , 2013, Text mining: concepts, process, and applications, Journal of Global Research in Computer Sciences, 4, 36-39. [20] Law, K. K., & Mills, L. F. , 2015, Taxes and nancial constraints: Evidence from linguisticcues, Journal of Accounting Research, 53(4), 777819. [21] Li, Y., & Wang, Y. , 2018, Machine learning methods of bankruptcy prediction using accounting ratios, Open Journal of Business and Management, 6, 1-20. [22] Leshno, M., & Spector, Y. , 1996, Neural network prediction analysis: the bankruptcy case, Neurocomputing, 10, 125-147. [23] Levin, A., Lin, C.F., & Chu, C-S.J. , 2002, Unit root test in panel data, Journal of Econometrics, 108, 1-22. [24] Lohmann, C., & Ohliger, T. , 2020, Bankruptcy prediction and the discriminatory power of annual reports: empirical evidence from nancially distressed German companies, Journal of Business Economics, 90(1), 137-172. [25] Lopatta, K., Gloger, M. A., & Jaeschke, R. , 2017, Can language predict bankruptcy? The explanatory power of tone in 10K lings, Accounting Perspectives, 16(4), 315-343. [26] Loughran, T., & McDonald, B. , 2011, When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks, The Journal of Finance, 66(1), 3565. [27] Marti Hearst, What is Text Mining?, https://people.ischool.berkeley.edu/ hearst/text- mining.html. [28] Mayew, W.J., Sethuraman, M., & Venkatachalam, M. , 2015, MD&A Disclosure and the Firm's Ability to Continue as a Going Concern, The Accounting Review, 90(4), 1621-1651. [29] Merkl-Davies, D. M., Brennan, N. M., 2007, Discretionary Disclosure Strategies in Corporate Narratives: Incremental Information or Impressions Management?, Journal of Accounting Literature, 27, 116- 196. [30] Mirali, M., Gholami moghaddam, F., Hesarzadeh, R. , 2018, Investigation of the Relationship between Financial Reporting Tone with Future Corporate Performance and Market Return, Financial Accounting Knowledge, 5(3), 81-98. (In Persian) [31] Najafzadeh, A., Farzinvash, A., Yosefi Sheikhrobat, M. & Naser, A. , 2021, Asymmetric behavior of the effectiveness of scal policies in the smooth transition process, Doctoral dissertation,Tehran, Mo d University. [32] NieSSner, T., Gross, D. H., & Schumann, M. , 2022, Evidential Strategies in Financial Statement Analysis: A Corpus Linguistic Text Mining Approach to Bankruptcy Prediction, Journal of Risk and Financial Management, 15(10), 459. [33] Odom, M.D., & Sharda, R. , 1990, A neural network model for bankruptcy prediction, Joint international conference on neural networks, 2, 163-168. [34] Ohlson, J.A. , 1980, Financial ratios and the probabilistic prediction of bankruptcy, Journal of accounting research, 18, 109-131. [35] Pervan, I., & Kuvek, T. , 2013, The relative importance of nancial ratios and non nancial variables in predicting insolvency, Croatian Operational Research Review, 4, 187-197. [36] Rowland, Z., Kasych, A., & Suler, P., 2021, Prediction of nancial distress: case of mining enterprises in Czech Republic, Ekonomicko-manazerske spektrum, 15(1), 1-14. [37] Shaw, M.J., & Gentry, J.A. , 1990, Inductive learning for risk classi cation, IEEE expert, 5, 47-53. [38] Shin, K., Lee, T.S., & Kim, H., 2005, An application of support vector machines in the bankruptcy prediction model, Expert syst. Appl., 28, 127-135. [39] Siano, F., & Wysocki, P., 2021, Transfer learning and textual analysis of accounting disclosures: Applying big data methods to small (er) datasets, Accounting Horizons, 35(3),217-244. [40] Visvanathan, G. , 2021, Is information in deferred tax valuation allowance useful in pre- dicting the rms ability to continue as a going concern incremental to MD&A disclosures and auditors going concern opinions?, International Journal of Disclosure and Governance,18(3), 223-239. [41] Yekini, L. S., Wisniewski, T. P., & Millo, Y. , 2016, Market reaction to the positiveness of annual report narratives, The British Accounting Review, 48(4), 415430. [42] Zhang, Z., Luo, M., Hu, Z., & Niu, H. , 2022, Textual Emotional Tone and Financial Crisis Identi cation in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning, Applied Sciences, 12(13). [43] Zhao, Y., Wei, S., Guo, Y., Yang, Q., & Kou, G. , 2022, FisrEbp: Enterprise Bankruptcy Prediction via Fusing its Intra-risk and Spillover-Risk. | ||
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