واکاوی عوامل مؤثر بر ورشکستگی مالی با رویکرد فراترکیب
محورهای موضوعی : مدیریت مالی بنگاه‌های اقتصادیعلی لعل بار 1 , مریم شریف نژاد 2 , مریم اسدی 3
1 - گروه حسابداری، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.
2 - گروه اقتصاد، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.
3 - گروه حسابداری، واحد اراک، دانشگاه آزاد اسلامی، اراک، ایران.
کلید واژه: آنتروپی شانون, فراترکیب, ورشکستگی,
چکیده مقاله :
هدف: در عصر حاضر ورشکستگی شرکت‌های بزرگ جهان باعث شده ورشکستگی مالی به‌عنوان موضوعی بسیار مهم در حوزه مالی مطرح و مطالعه علل مؤثر در ورشکستگی از نگاه مالی و همچنین ارزیابی ورشکستگی به کمک الگوهای متداول علمی بسیار برجسته گردد. بنابراین هدف مطالعه حاضر واکاوی عوامل مؤثر بر ورشکستگی مالی است.
روش‌شناسی پژوهش: به این منظور با استفاده از رویکرد فراترکیب نتایج و یافته‌های پژوهشهای پیشین (170 مقاله علمی) در سال 1400 مورد ارزیابی قرارگرفت. سپس با استفاده از روش کمی آنتروپی شانون ضرایب عوامل مؤثر بر ورشکستگی تعیین گردید.
یافته‌ها: نتایج تحقیق حاضر حاکی از آن است که میتوان عوامل مؤثر بر ورشکستگی را براساس چهار بعد یعنی عوامل مالی، عوامل غیرمالی، عوامل داخلی و عوامل خارجی دستهبندی و در قالب 27 متغیر شناسایی نمود. از طرفی این تحقیق گویای این حقیقت است که متغیرهایی مثل بازده دارایی، نسبت جاری، نسبت حقوق صاحبان سهام به کل دارایی و نسبت سرمایه در گردش به مجموع دارایی از مهمترین عوامل مؤثر بر ورشکستگی شرکتها میباشند. در حالیکه عواملی نظیر ارزش دفتری هر سهم و موجودی کالا به بدهی جاری پایینترین رتبه را از نظر اهمیت به خود اختصاص دادهاند. یعنی در بین مدلها و پژوهشهای مختلف مورد مطالعه، توجه به عوامل و تکرارپذیری این متغیرها کمتر است.
اصالت / ارزش‌افزوده علمی: بنابراین به سرمایهگذاران، سهامداران و مدیران مالی مؤسسات پیشنهاد میگردد در تصمیمات مالی خود به عواملی مؤثر در وقوع ورشکستگی مالی که در این تحقیق شناسایی شده توجه ویژه داشته باشند.
Purpose: In the contemporary era, the bankruptcy of major global corporations has highlighted financial distress as a highly significant issue in the field of finance. Consequently, the study of factors influencing bankruptcy from a financial perspective, along with the evaluation of bankruptcy through well-established scientific models, has become increasingly important. Therefore, the objective of the present study is to investigate the determinants of financial bankruptcy.
Methodology: To achieve this purpose, a meta-synthesis approach was applied to evaluate the results and findings of prior research (170 academic articles) conducted in 2021 (1400 in the Iranian calendar). Subsequently, using the quantitative Shannon entropy method, the weights of the factors affecting bankruptcy were determined.
Findings: The results indicate that the factors influencing bankruptcy can be categorized into four dimensions: financial, non-financial, internal, and external. Within these dimensions, 27 variables were identified. Moreover, the findings reveal that variables such as return on assets, current ratio, equity-to-total-assets ratio, and working capital-to-total-assets ratio are among the most critical determinants of corporate bankruptcy. Conversely, variables such as book value per share and inventory-to-current-liabilities ratio were ranked lowest in importance, reflecting that these factors received less attention and were less consistently repeated across the reviewed studies and models.
Originality / Value: This study suggests that investors, shareholders, and financial managers should pay special attention to the key determinants of financial bankruptcy identified herein when making financial decisions.
Abedi Jafari, A., & Amiri, M. (2019). Meta-Synthesis as a Method for Synthesizing Qualitative Researches. Methodology of Social Sciences and Humanities, 25(99), 73-87. [In Persian]
Acosta-González, E., Fernández-Rodríguez, F., & Ganga, H. (2019). Predicting corporate financial failure using macroeconomic variables and accounting data. Computational Economics, 53, 227-257.
Ahmadpour, A., Shahsavari, M., & amoozad Khalili, A. (2016). Investigation of Important Factors on Risk of Financial Bankruptcy. Empirical Studies in Financial Accounting, 13(51), 9-34. [In Persian]
Alipour, R., Sheikhi, M., & Agajani, V. (2019). The moderating effect of firm size on the relationship between capital structure and financial distress in the companies accepted in Tehran Stock Exchange. Journal of Accounting and Management Vision, 2(14), 1–18. [In Persian]
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.
Appiah, K. O., & Amon, C. (2017). Board audit committee and corporate insolvency. Journal of Applied Accounting Research, 18(3), 298-316.
Arab Mazar Yazdi, M., & Safar Zadeh, M. H. (2010). Investigation into the ability of financial ratios in predicting financial distress: Logit analysis. Journal of Securities Exchange, 2(8), 7–37. [In Persian]
Aragon, G. O., & Strahan, P. E. (2012). Hedge funds as liquidity providers: Evidence from the Lehman bankruptcy. Journal of Financial Economics, 103(3), 570-587.
Argenti, J. (1976). Company failure: The tell-tale signs at the top. Management Review, 65(2).
Azar, A. (2001). Development of Shannon entropy method for data processing in content analysis. Journal of Humanities of Al-Zahra University, 11(37-38), 1-18. [In Persian]
Bahiraie, A., Etemadi, K., & Gerami asl, A. (2016). Predicting Companies Financial bankruptcy Listed in Tehran Stock Exchange using ANN, ANFIS, LOGIT. New Marketing Research Journal, 6(2), 166-153. [In Persian]
Bani Mahd, B., & Akbari, R. (2010). Investigation of the association between Z’Altmans bankruptcy index and auditor change. Management Accounting, 3(6), 41–47. [In Persian]
Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405-417.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of accounting research, 71-111.
Biddle, G. C., Ma, M. L., & Song, F. M. (2022). Accounting conservatism and bankruptcy risk. Journal of Accounting, Auditing & Finance, 37(2), 295-323.
Brewer, B. E., Wilson, C. A., Featherstone, A. M., Harris, J. M., Erickson, K., & Hallahan, C. (2012). Measuring the financial health of US production agriculture. Journal of ASFMRA, 178-193.
Brogaard, J., & Detzel, A. (2015). The asset-pricing implications of government economic policy uncertainty. Management science, 61(1), 3-18.
Cenciarelli, V. G., Greco, G., & Allegrini, M. (2018). Does intellectual capital help predict bankruptcy?. Journal of intellectual capital, 19(2), 321-337.
Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert systems with applications, 38(9), 11261-11272.
Christidis, A. C. Y., & Gregory, A. (2010). Some new models for financial distress prediction in the UK.
Dabagh, R., & Sheikhbeiglou, S. (2021). Bankruptcy Prediction of listed Companies in Tehran’s Stock Exchange by Artificial Neural Network (ANN) and Fulmer Model. Journal of Development and Capital, 5(2), 153-168. [In Persian]
Dadbeh, F., & Partovifar, Z. (2021). The impact of disclosure of auditor reporting on business failure. Professional Auditing Research, 1(2), 106-131. [In Persian]
Dakovic, R., Czado, C., & Berg, D. (2010). Bankruptcy prediction in Norway: a comparison study. Applied economics letters, 17(17), 1739-1746.
Dankiewicz, R., & Simionescu, M. (2020). The insurance market in Romania: A macroeconomic and a microeconomic approach. Transformations in Business & Economics, 19(1).
Du Jardin, P., & Séverin, E. (2011). Predicting corporate bankruptcy using a self-organizing map: An empirical study to improve the forecasting horizon of a financial failure model. Decision Support Systems, 51(3), 701-711.
Esmaeilzadeh Moghari, A., & Shakeri, H. (2015). Predicting financial distress of companies listed on the Tehran Stock Exchange using a naive Bayesian network and comparing it with data envelopment analysis. Financial Engineering and Portfolio Management, 6(22), 1–28. [In Persian]
Faghekarimi, S., Ohadi, F., Nikomram, H., & Royaei, R. (2022). Evaluating the Effect of Managers' Narcissism on Bankruptcy Risk Among Companies Listed on the Tehran Stock Exchange. Studia Universitatis Vasile Goldiș Arad, Seria Științe Economice, 32(2), 65-83.
Firouzian, M., Javid, D., & Najmadini, N. (2011). The Application of Genetic Algorithms in Bankruptcy Predication and the Comparison of it with Altman’s Z-model listed companies in Tehran Stocks Exchange (TSE). Accounting and Auditing Review, 18(65), 99-114. [In Persian]
Ghadiri Moghadam, A., Gholampour Fard, M. M., & Nasir Zadeh, F. (2010). Investigating the ability of Altman and Ohlson bankruptcy prediction models in predicting the bankruptcy of companies listed on the Tehran Stock Exchange. Monetary & Financial Economics, 16(28), 193–220. [In Persian]
Ghamari Moghaddam, A., Lari Dasht Bayaz, M., & Nakhaei, H. (2022). The relationship among the cash components of profit, the stability of profit and the probability of bankruptcy of companies listed in Tehran Stock Exchange. Advances in Finance and Investment, 3(8), 61-86. [In Persian]
Ghodrati, H., & Manavi Moghadam, A. H. (2010). Investigation of Bankruptcy Prediction Models) Altman, Shirata, Ahlson, Zemsky, Springer, CI Scor, Fulmer, Farajzadeh Genetics, and McCabe Genetics Models (Tehran Stock Exchange). Accounting and Auditing Research, 2(7), 128-140. [In Persian]
Haji Hashem, M., & Amir Hosseini, Z. (2019). Bankruptcy prediction and Corporate Governance: Financial Ratio Approach. Journal of Management Accounting and Auditing Knowledge, 8(30), 201-220. [In Persian]
Hajiha, Z., & Ghaem Maghami, M. (2012). Investigation on the role of conservative accounting in the reduction of company bankruptcy risk (Evidence from Iranian capital market, based on Zawgin bankruptcy model). Management Accounting, 5(13), 1–15. [In Persian]
Heydary Farahany, M., Ghayour, F., & Mansourfar, G. (2019). The effect of management behavioral strains on financial distress. Financial Accounting Research, 11(3), 117-134. [In Persian]
Holcomb, T. R., Holmes Jr, R. M., & Connelly, B. L. (2009). Making the most of what you have: Managerial ability as a source of resource value creation. Strategic management journal, 30(5), 457-485.
Hosseini, S., & Rashidi, Z. (2013). Bankruptcy Prediction of Companies listed Corporations in Tehran Stock Exchange by Using Decision Tree and Logistic Regression. Financial Accounting Research, 5(3), 105-128. [In Persian]
Hu, H., & Sathye, M. (2015). Predicting financial distress in the Hong Kong growth enterprises market from the perspective of financial sustainability. Sustainability, 7(2), 1186-1200.
Hui, K. W., Klasa, S., & Yeung, P. E. (2012). Corporate suppliers and customers and accounting conservatism. Journal of accounting and economics, 53(1-2), 115-135.
Imani, S., Tasaddi Kari, M.J. (2023). Explanation of the comprehensive pattern of bankruptcy. Advances in Finance and Investment, 4(3), 153-180. [In Persian]
Iraji Rad, A., & Dehbashi, S. (2014). Investigating some factors affecting bankruptcy. The Third National Conference on Accounting, Financial Management, and Investment, Gorgan, Iran. [In Persian]
Jencova, S., Petruska, I., Lukacova, M., & Abu-Zaid, J. (2021). Prediction of bankruptcy in non-financial corporations using neural network. Montenegrin Journal of Economics, 17(4), 123-134.
Jia, Z., Shi, Y., Yan, C., & Duygun, M. (2020). Bankruptcy prediction with financial systemic risk. The European Journal of Finance, 26(7-8), 666-690.
Karimi Pashaki, M., & Ahadzadeh Namin, M. (2022). Financial bankruptcy forecasting model with a two-tier approach in data envelopment analysis with semi-positive and negative indicators. Journal of Decisions and Operations Research, 7(4), 581-595. [In Persian]
Karimi, M., & Saifi, G. (2023). The Effect of Ownership Concentration on the Relationship between Auditor's Abnormal Fee and Bankruptcy Risk. Journal of Accounting and Management Vision, 5(71), 53-65. [In Persian]
Khajavi, S., & Amiri, F. S. (2012). Recognition of Efficient Factors Affecting in companies’ bankruptcy using TOPSIS_AHP. Empirical Studies in Financial Accounting, 10(38), 69-90. [In Persian]
Khajavi, S., & Ghadirian Arani, M. (2018). Managerial ability, financial performance, and bankruptcy risk. Journal of Accounting Knowledge, 9(1), 35–61. [In Persian]
Kim, M. J., & Kang, D. K. (2012). Classifiers selection in ensembles using genetic algorithms for bankruptcy prediction. Expert Systems with applications, 39(10), 9308-9314.
Kordestani, G., Tatli, R., & Kosari Far, H. (2014). The evaluate ability of Altman adjusted model to predict stages of financial distress, Newton, and bankruptcy. Journal of Investment Knowledge, 3, 83–100. [In Persian]
Landsman, W. R., Nelson, K. K., & Rountree, B. R. (2009). Auditor switches in the pre‐and post‐Enron eras: Risk or realignment?. The Accounting Review, 84(2), 531-558.
Lawshe, C. H. (1975). A Quantitative Approach to Content Validity. Personnel psychology/Berrett-Koehler Publishers.
Leng, J., Ozkan, A., Ozkan, N., & Trzeciakiewicz, A. (2021). CEO overconfidence and the probability of corporate failure: evidence from the United Kingdom. The European Journal of Finance, 27(12), 1210-1234.
Liang, D., Lu, C. C., Tsai, C. F., & Shih, G. A. (2016). Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study. European journal of operational research, 252(2), 561-572.
Liu, L., & Zhang, T. (2015). Economic policy uncertainty and stock market volatility. Finance Research Letters, 15, 99-105.
Maggina, A. (2008). Auditors' switching: An empirical investigation. Global Journal of Business Research, 2(1).
Makian, S. N. A. D., & Karimi Takalou, S. (2009). Bankruptcy predicting of firms using artificial neural network (The case study in the province of Kerman). Journal of Quantitative Economics (Quarterly Journal of Economics Review), 6(1(20)), 129–144. [In Persian]
Mehrani, S., Kamyabi, Y., & Ghayour, F. (2017). Reviewing the Effectiveness of Earnings Quality Indices on the Power of Financial Distress Prediction Models. Accounting and Auditing Review, 24(1), 103-126. [In Persian]
Mehrani, S., Mehrani, K., Monsefi, Y., & Karami, G. (2005). Practical investigation of Zmijewski and Shirata bankruptcy prediction models in companies listed on the Tehran Stock Exchange. Accounting and Auditing Review, 12(3), 105-131. [In Persian]
Moradi Shahdadi, K., Anvary Rostamy, A. A., Ranjbar, M. H., & Sadeghi Sharif, S. J. (2018). Explaining impacts of intellectual capital on reducing firms’ probability of bankruptcy: Evidence from Tehran Stock Exchange. Operations Research and Management Research, 7(4), 157–159. [In Persian]
Najarpoor Hasani, M., & Khanlari, M. (2020). Predicting financial helplessness in companies listed on the Tehran Stock Exchange with emphasis on accruals and cash flows. Journal of Accounting and Management Vision, 3(24), 94-107. [In Persian]
Nazemi Ardakani, M., Zare MehrJardi, V., & Mohammadi-Nodooshan, A. (2018). A firms' bankruptcy prediction model based on selected industries by using decision trees model. Advanced Mathematical Finance, 6(2), 121–138. [In Persian]
Newton, G. W. (2009). Bankruptcy and Insolvency Accounting, Volume 1: Practice and Procedure (Vol. 1). John Wiley & Sons.
Nishihara, M., & Shibata, T. (2021). The effects of asset liquidity on dynamic sell-out and bankruptcy decisions. European Journal of Operational Research, 288(3), 1017-1035.
Olson, D. L., Delen, D., & Meng, Y. (2012). Comparative analysis of data mining methods for bankruptcy prediction. Decision Support Systems, 52(2), 464-473.
Pasternak-Malicka, M., Ostrowska-Dankiewicz, A., & Dankiewicz, R. (2021). Bankruptcy-an assessment of the phenomenon in the small and medium-sized enterprise sector-case of Poland. Polish journal of management studies, 24(1), 250-267.
Pastor, L., & Veronesi, P. (2012). Uncertainty about government policy and stock prices. The journal of Finance, 67(4), 1219-1264.
Pindado, J., Rodrigues, L., & De la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61(9), 995-1003.
Premachandra, I. M., Chen, Y., & Watson, J. (2011). DEA as a tool for predicting corporate failure and success: A case of bankruptcy assessment. Omega, 39(6), 620-626.
Raei, R., & Fallahpour, S. (2009). Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios. Accounting and Auditing Review, 15(4), 17-34. [In Persian]
Rafiei, F. M., Manzari, S. M., & Bostanian, S. (2011). Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert systems with applications, 38(8), 10210-10217.
Rahimian, N., Salehirad, M., & Mohammadi, H. (2010). The Relationship between Accounting Conservatism and Bankruptcy Risk in TSE'S Listed Companies. Empirical Studies in Financial Accounting, 8(30), 127-149. [In Persian]
Rinofah, R., Kusumawardhani, R., & Putri, V. A. M. (2022). Factors affecting potential company bankruptcy during the covid-19 pandemic. Jurnal Keuangan dan Perbankan, 26(1), 208-228.
Robinson, D., Robinson, M., & Sisneros, C. (2012). Bankruptcy outcomes: Does the board matter?. Advances in accounting, 28(2), 270-278.
Sadeghi, H., Rahimi, P., & Salmani, Y. (2014). The Effect of Macroeconomic and Governance Factors on Financial Distress in Manufacture Firms Listed in Tehran Stock Exchange. Monetary & Financial Economics, 21(8), 107-127. [In Persian]
Saghafi, A., & Motamedi Fazel, M. (2014). Investigate of the Relation between Accounting Conservatism (Unconditional and Conditional) and Bankruptcy Risk. Financial Accounting Research, 6(2), 1-16. [In Persian]
Sandelowski, M., & Barroso, J. (2003). Toward a metasynthesis of qualitative findings on motherhood in HIV‐positive women. Research in nursing & health, 26(2), 153-170.
Serrano-Cinca, C., Gutiérrez-Nieto, B., & Bernate-Valbuena, M. (2019). The use of accounting anomalies indicators to predict business failure. European Management Journal, 37(3), 353-375.
Setyesh, M. H., & Rahimi, M. (2023). The impact of accounting information quality and monetary policy on bankruptcy prediction. Judgment and Decision Making in Accounting, 2(5), 1-38. [In Persian]
Sousa, A. M. J., Braga, A. C., & Cunha, J. (2022). Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector.
Stolbov, M., & Shchepeleva, M. (2020). Systemic risk, economic policy uncertainty and firm bankruptcies: Evidence from multivariate causal inference. Research in International Business and Finance, 52, 101172.
Sun, J., Jia, M. Y., & Li, H. (2011). AdaBoost ensemble for financial distress prediction: An empirical comparison with data from Chinese listed companies. Expert systems with applications, 38(8), 9305-9312.
Taj mazinani, M., Fallahpour, S., & Bajalan, S. (2015). The Use of Feature Selection Method (HARC) in Predicting Financial Distress in Tehran Stock Exchange. Financial Management Strategy, 3(2), 77-106. [In Persian]
Thahir Abdul Nasser, A., Abdul Wahid, E., Nazatul Faiza Syed Mustapha Nazri, S., & Hudaib, M. (2006). Auditor‐client relationship: the case of audit tenure and auditor switching in Malaysia. Managerial Auditing Journal, 21(7), 724-737.
Tinoco, M. H., & Wilson, N. (2013). Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables. International review of financial analysis, 30, 394-419.
Vaghfi, S. H. (2019). Using artificial intelligence algorithm in Financial Bankruptcy by Macro-economic and Accounting variables in listed companies for stock exchange in Tehran. Journal of Decisions and Operations Research, 4(2), 158-173. [In Persian]
Wing, Y., Fanny, H., Law, E., & Fung, L. (2003). An Analysis of the financial health of Hong Kong corporations. Retrieved Augost, 1, 2019.
Zahmatkesh, J., Taftiyan, A., Moeinadin, M., & Nezarat, A. (2023). Systematic review of bankruptcy prediction models. Advances in Finance and Investment, 4(4), 117-144. [In Persian]
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting research, 59-82.
Zohra, K. F., Mohamed, B., Elhamoud, T., Garaibeh, M., Ilhem, A., & Naimi, H. (2015). Using Financial Ratios to Predict Financial Distress of Jordanian Industrial Firms-''Empirical Study Using Logistic Regression''. Academic journal of interdisciplinary studies, 4(2), 136-142.
