ابراهیمی سرو علیا، محمدحسن؛ باباجانی، جعفر؛ آخوند، محمدرضا و فاخر، اسلام (1397). ارائه الگویی برای پیش‌بینی پویای درماندگی مالی با استفاده از تحلیل بقاء.
فصلنامه اقتصاد مقداری، 15(4)، 167-198.
https://jqe.scu.ac.ir/article_14147.html.
بت‌شکن، محمد هاشم؛ سلیمی، محمدجواد و فلاحتگر متحدجو، سعید (1397). ارائه یک روش ترکیبی به‌منظور پیش‌بینی درمانـدگی مالی شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران.
تحقیقات مالی، 20(2)، 173-192.
https://jfr.ut.ac.ir/article_67697.html.
برزگری خانقاه، جمال؛ انصاری سامانی، حبیب و رزاززاده، لیدا (1398). پیش‌بینی وضعیت مالی شرکت‌ها با استفاده از تحلیل محتوای گزارش‌های هیئت‌مدیره.
مطالعات تجربی حسابداری مالی، 16(64)، 135-160.
https://qjma.atu.ac.ir/article_10959.html.
پله، مولود؛ ایزدی نیا، ناصر و امیری، هادی (1398). بررسی تأثیر لحن گزارش‌های فعالیت هیئت‌مدیره بر عملکرد آتی شرکت‌ها مبتنی بر دو دیدگاه علامت‌دهی و رفتار فرصت‌طلبانه مدیران.
دو فصلنامه حسابداری ارزشی و رفتاری، ۴(۸)، ۱-۳۱.
http://aapc.khu.ac.ir/article-1-663-fa.html.
حسینی، سید رسول و حاجیان نژاد، امین (1401). تأثیر توزیع آماری نسبت‌های مالی بر مقادیر مدل آلتمن با استفاده از شبیه‌سازی مونت‌کارلو.
مجله دانش حسابداری، 13(3)، 161-193.
https://jak.uk.ac.ir/article_3168.html.
رهروی دستجردی، علیرضا، فروغی، داریوش، کیانی، غلامحسین. (1397). ارزیابی خطر تقلب مدیران با استفاده از روش داده‌کاوی.
مجله دانش حسابداری، 9(33)، 114-91.
https://jak.uk.ac.ir/article_1932.html.
عباسیان، عزت اله؛ شهرکی، کاوه؛ فلاح‌پور، سعید و نمکی، علی (1402). رویکردی نوین در پیش‌بینی درماندگی مالی با به‌کارگیری اطلاعات مبتنی بر شبکه مالی و روش ترکیبی درخت تصمیم تقویت گرادیان.
مدیریت دارایی و تأمین مالی، 3(42)، 113-140.
https://ensani.ir/fa/article/583773.
مهرانی، ساسان؛ مهرانی، کاوه؛ منصفی، یاشار و کرمی، غلامرضا (1384). بررسی کاربردی الگوهای پیش‌بینی ورشکستگی زیمسکی و شیرانا در شرکت‌های پذیرفته شده در بورس اوراق بهادار تهران.
بررسیهای حسابداری و حسابرسی، 41، 105-131.
https://acctgrev.ut.ac.ir/article_18462.html.
نمازی، محمد و ابراهیمی، شهلا (1400). پیش‌بینی درماندگی مالی شرکت‌های پذیرفته شده در فرابورس و بورس اوراق بهادار تهران با استفاده از ماشین بردار پشتیبان.
راهبرد مدیریت مالی، 9(32)، 115-132.
https://jfm.alzahra.ac.ir/article_5588.html.
هاشمی گل سفیدی، افشین؛ لشگری، زهرا و حاجیها، زهره (1401). کاربرد یادگیری ماشین در ارائه الگویی برای پیش‌بینی ورشکستگی.
تحقیقات حسابداری و حسابرسی، 14(56)، 171-190. DOI:
10.22034/iaar.2022.168271.
References
Abbasian, E., Shahraki, K., Fallahpour, S., & Namaki, A. (2023). A novel approach in predicting financial distress by financial network-based information and the integrated method of gradient boosting decision tree.
Journal of Asset Management and Financing,
https://ensani.ir/fa/article/583773 [In Persian].
Aflatoni, A. (2013).
Statistical Analysis with Eviews in accounting and financial management research. Tehran, Termeh Publications.
https://www.gisoom.com/book/11397041 [In Persian].
Altman E.I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy.
The Journal of Finance, 23(4), 589-609.
https://doi.org/10.2307/2978933.
Altman, E.I., Iwanicz‐Drozdowska, M., Laitinen, E.K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model.
Journal of International Financial Management & Accounting, 28(2), 131-171.
https://doi.org/10.1111/jifm.12053.
Balakrishnan, R., Qiu, X.Y., & Srinivasan, P. (2010). On the predictive ability of narrative disclosures in annual reports.
European Journal of Operational Research, 202(3), 789-801.
https://doi.org/10.1016/j.ejor.2009.06.023.
Barzegari Khanagha, J., Ansari Samani, H., & Razzazzadeh, L. (2019). Predicting the financial status of companies using content analysis the reports of the board of directors.
Empirical Studies in Financial Accounting, 16(64), 135-160.
https://qjma.atu.ac.ir/article_10959.html?lang=en [In Persian].
Beaver, W.H. (1966). Financial ratios as predictors of failure, empirical research in accounting: Selected studies.
Journal of Accounting Research, 5, 71-111.
https://doi.org/10.2307/2490171.
Botshekan, M., Salimi, M., & Falahatgar Mottahedjoo, S. (2018). Developing a hybrid approach for financial distress prediction of listed companies in Tehran stock exchange.
Financial Research Journal, 20(2), 173-192.
https://jfr.ut.ac.ir/article_67697.html?lang=en [In Persian].
Cole, C.J., & Jones, C.L. (2005). Management discussion and analysis: A review and implications for future research.
Journal of Accounting Literature, 24, 135. [
ProQuest].
Deakin, E.B. (1972). A discriminant analysis of predictors of business failure.
Journal of Accounting Research, 10(1), 167-179.
https://doi.org/10.2307/2490225.
Ebrahimi sarv olia, M.H., Babajani, J., Akhond, M., & Fakher, E. (2019). A pattern for dynamic prediction of financial distress by using survival analysis.
Quarterly Journal of Quantitative Economics, 15(4), 167-198.
https://jqe.scu.ac.ir/article_14147.html [In Persian].
Fitzpatrick, P. (1932). A comparison of ratios of successful industrial enterprises with those of failed companies. The certified public accountant (October, November, December), 598–605, 656–662, and 727–731, respectively.
https://www.sciepub.com/reference/297635.
Guo, L., Shi, F., & Tu, J. (2016). Textual analysis and machine leaning: Crack unstructured data in finance and accounting.
The Journal of Finance and Data Science, 2(3), 153-170.
https://doi.org/10.1016/j.jfds.2017.02.001.
Hajek, P., & Henriques, R. (2017). Mining corporate annual reports for intelligent detection of financial statement fraud–A comparative study of machine learning methods
. Knowledge-Based Systems, 128, 139-152.
https://doi.org/10.1016/j.knosys.2017.05.001.
Hashemi Gelsefidi, A., Lashgari, Z., & Hajiha, Z. (2022). Using machine learning to provide a model for predicting bankruptcy.
Accounting and Auditing Research, 14(56), 171-190. DOI:
10.22034/iaar.2022.168271 [In Persian].
Hackeling, G. (2017). Mastering machine learning with scikit-learn. Packt Publishing Ltd. [
Amazon].
Henry, E., & Leone, A.J. (2016). Measuring qualitative information in capital markets research: Comparison of alternative methodologies to measure disclosure tone.
The Accounting Review, 91(1), 153-178.
https://doi.org/10.2308/accr-51161.
Hosseini, S.R., & Hajiannejad, A. (2022). The effect of statistical distribution of financial ratios on Altman model values using Monte Carlo simulation.
Journal of Accounting Knowledge, 13(3), 161-193.
https://jak.uk.ac.ir/article_3168.html?lang=en [In Persian].
Huang, B., Yao, X., Luo, Y., & Li, J. (2023). Improving financial distress prediction using textual sentiment of annual reports.
Annals of Operations Research, 330(1), 457-484.
https://doi.org/10.1007/s10479-022-04633-3.
Humpherys, S.L., Moffitt, K.C., Burns, M.B., Burgoon, J.K., & Felix, W.F. (2011). Identification of fraudulent financial statements using linguistic credibility analysis.
Decision Support Systems, 50(3), 585-594.
https://doi.org/10.1016/j.dss.2010.08.009.
Jo, N.O., & Shin, K.S. (2016). Bankruptcy prediction modeling using qualitative information based on big data analytics.
Journal of Intelligence and Information Systems, 22(2), 33-56.
https://doi.org/10.13088/jiis.
Kang, T., Park, D.H., & Han, I. (2018). Beyond the numbers: The effect of 10-K tone on firms’ performance predictions using text analytics.
Telematics and Informatics, 35(2), 370-381.
https://doi.org/10.1016/j.tele.2017.12.014.
Li, F. (2010). Textual analysis of corporate disclosures: A survey of the literature.
Journal of Accounting Literature, 29, 143–165.
https://ssrn.com/abstract=1756926.
Li, S., Shi, W., Wang, J., & Zhou, H. (2021). A deep learning-based approach to constructing a domain sentiment lexicon: a case study in financial distress prediction.
Information Processing & Management, 58(5), 102673.
https://doi.org/10.1016/j.ipm.2021.102673.
Li, J., & Wang, C. (2023). A deep learning approach of financial distress recognition combining text.
Electronic Research Archive, 31(8), 4683-4707. DOI:
10.3934/era.2023240.
Mai, F., Tian, S., Lee, C., & Ma, L. (2019). Deep learning models for bankruptcy prediction using textual disclosures.
European Journal of Operational Research, 274(2), 743-758.
https://doi.org/10.1016/j.ejor.2018.10.024.
Mehrani, S., & Nonahal Nahr, A.A. (2013). Evaluating the effect of the judgmental approaches of language in reducing audit expectation gap.
Accounting and Auditing Review, 20(3), 123-144. DOI:
10.22059/acctgrev.2013.35679 [In Persian].
Mehrani, S., Mehrani, K., Monsefi, Y., & Karami, Gh. (2005). An application study of Zimsky and Shirana bankruptcy prediction models in companies listed on the Tehran Stock Exchange.
Accounting and Auditing Reviews, 41, 105-131
. https://acctgrev.ut.ac.ir/article_18462.html?lang=en [In Persian].
Merwin, C.L. (1942). Financing small corporations in five manufacturing industries, 1926–36. [
NBER Books].
Namazi, M., & Ebrahimi, S. (2021). Financial distress prediction of the listed companies on Tehran Stock Exchange (TSE) and Iran Fara Burse (IFB) using support vector machine.
Financial Management Strategy, 9(1), 115-132.
https://jfm.alzahra.ac.ir/article_5588.html?lang=en [In Persian].
Peleh, M., Izadinia, N., & Amiri, H. (2020). The effect of tone in board's activity reports on future performance based on signaling and opportunistic approach.
Iranian Journal of Behavioral and Valued Accounting, 4(8), 1-31.
http://aapc.khu.ac.ir/article-1-663-fa.html [In Persian].
Rahrovi Dastjerdi, A., Foroghi, D., & Kiani, G. (2018). Assessing managers fraud through analysis of board of directors report by data mining.
Journal of Accounting Knowledge, 9(1), 91-114.
https://jak.uk.ac.ir/article_1932.html [In Persian].
Song, Y., Jiang, M., Li, S., & Zhao, S. (2024). Class‐imbalanced financial distress prediction with machine learning: Incorporating financial, management, textual, and social responsibility features into index system.
Journal of Forecasting, 43(3), 593-614.
https://doi.org/10.1002/for.3050.
Sreedharan, M., Khedr, A.M., & El Bannany, M. (2020). A comparative analysis of machine learning classifiers and ensemble techniques in financial distress prediction.
In 2020 17th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 653-657.
https://doi.org/10.1109/SSD49366.2020.9364178.
Tang, X., Li, S., Tan, M., & Shi, W. (2020). Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods.
Journal of Forecasting, 39(5), 769-787.
https://doi.org/10.1002/for.2661.
Ohlson, J.A. (1980). Financial ratios and the probabilistic prediction of bankruptcy.
Journal of Accounting Research, 109-131.
https://doi.org/10.2307/2490395.
Wang, Z., Chen, M.H., Chin, C.L., & Zheng, Q. (2017). Managerial ability, political connections, and fraudulent financial reporting in China.
Journal of Accounting and Public Policy, 36(2), 141-162.
https://www.researchgate.net/publication/315499649.
Zare Bahnamiri, M.J., Heidary Surshjani, Z., & Joudaki Chegeni, Z. (2020). CEO power and earnings announcement tone with emphasis on the role of board oversight.
Iranian Journal of Behavioral and Valued Accounting, 11(44), 119-143.
https://qfaj.mobarakeh.iau.ir/article-1-2005-fa.html [In Persian].
Zmijewski, M.E. (1984). Methodological issues related to the estimation of financial distress prediction models.
Journal of Accounting Research, 22, 59-82.
https://doi.org/10.2307/2490859.
)