نقش سرمایه فکری در پیش‌بینی بحران مالی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد بخش حسابداری،دکتری حسابداری،دانشگاه شیراز

2 دکتری بخش حسابداری،دانشگاه شیراز

چکیده

چکیده
با توجه به افزایش اهمیت دارایی‌های نامشهود در اقتصاد دانش‌محور، هدف اصلی پژوهش حاضر بررسی نقش سرمایة فکری و اجزای آن (کارایی سرمایه به‌کار گرفته‌شده، کارایی سرمایة انسانی و کارایی سرمایة ساختاری) به‌عنوان معیارهای دارایی‌های نامشهود در پیش‌بینی بحران مالی است. در این راستا، با استفاده از داده‌های نمونه‌ای شامل 200 سال-شرکت بحران‌زده و 200 سال- شرکت سالم پذیرفته‌شده در بورس اوراق بهادار تهران در بازه زمانی 1394-1385 توان الگوهای مبتنی بر نسبت‌های مالی و مبتنی بر نسبت‌های مالی و سرمایة فکری برای پیش‌بینی بحران مالی با استفاده از آزمون من‌ویتنی مقایسه شده‌اند. یافته‌های پژوهش نشان داد دقت الگوهای پیش‌بینی با حضور کارایی سرمایة به‌کار گرفته‌شده تفاوت معنی‌داری با دقت الگوهای مبتنی بر نسبت‌های مالی ندارد. با وجود این، دقت الگوهای پیش‌بینی با حضور ضریب ارزش افزودة فکری، کارایی سرمایة انسانی و کارایی سرمایة ساختاری به‌طور معنی‌داری بیشتر از الگوهای مبتنی بر نسبت‌های مالی در روش‌های طبقه‌بندی‌کنندة تجمیعی بوستینگ و بگینگ است. به‌بیان دیگر، افزودن سرمایة فکری، سرمایة انسانی و سرمایة ساختاری به الگوهای پیش‌بینی بحران مالی، دقت آن‌ها را افزایش می‌دهد؛ همچنین یافته‌های فرعی پژوهش حاکی است هرچه فاصلة دورة زمانی پیش‌بینی با وقوع بحران مالی بیشتر باشد، افزودن معیارهای دارایی‌های نامشهود (سرمایة فکری، سرمایة انسانی و سرمایة ساختاری) دقت الگوهای پیش‌بینی را بیشتر افزایش می‌دهد.

کلیدواژه‌ها


عنوان مقاله [English]

The Role of Intellectual Capital in Financial Distress Prediction

نویسندگان [English]

  • Shokrollah Khajavi 1
  • Mohammad Hossein Ghadirian Arani 2
چکیده [English]

  Introduction
Today, with the increasing competition in business, the possibility of failure has increased. Due to the increase in the competition and also the growing importance of intangible assets, the importance of intellectual capital increases more than before. Accordingly, it is expected that lack of sufficient attention to the intellectual capital causes removal of companies from the competition and becoming bankrupt. In other words, intellectual capital can create competitive advantage and be a factor for success in business and better financial performance; accordingly, it can prevent financial distress in competitive market and knowledge based economy.
The Iraninan financial distress literature shows a high number of financial distress prediction models that are generally based on financial ratio, but not on the nonfiancial causes of failure. In fact, despite the importance of intangible assets in the success and failure of companies, these studies ignore the influence of these nonfinancial factors.
Our research attempts to fill the gap described above. Therefore, the main purpose of this study is to investigate the contribution of intellectual capital and its components as proxies of intangible asset in financial distress prediction.
 
Research Hypotheses
Based on the preceding theoretical framework regarding the role of intellectual capital and its components in financial distress prediction, research hypotheses were developed as follows:
H1: Including intellectual capital in financial distress models improves their predictive ability.
H2: Including components of intellectual capital in financial distress models improves their predictive ability.
 
Methods
Our study is a quantitative research that uses the scientific method and empirical evidence. The empirical data was collected from listed companies in the Tehran Stock Exchange, over the ten-year period of 2006 to 2015. Ensemble classifiers (Boosting and Bagging) were used as prediction methods for constructing financial distress prediction models. In order to test hypotheses, the accuracy of financial ratio–based models, and models utilizing both financial ratio and intangible assets measures (value added intellectual coefficient, human capital efficiency coefficient, and structural capital efficiency coefficient) was compared by using data of a sample of 200 financially distressed firm-years and 200 non-distressed firm-years.
 
Results
The major results of this study show that the accuracy of combined models is significantly more than financial ratio-based models in the both Boosting and Bagging methods. That is, intangible assets proxies improve the ability of financial ratio-based models to predict financial distress. Also, the findings of present study reveal that the more the space between prediction years and financial distress occurrence, the more the effect of intangible assets measures on accuracy of financial distress prediction models.
 
 
Discussion and Conclusion
Findings of the study support our conceptual framework about the effect of intellectual capital and its components on performance of financial distress models. That is, these findings imply that intangible assets (intellectual capital, human capital, and structural capital) are indeed valuable for financial distress prediction, so that adding these variables to financial based models improves their performance in predicting financial distress. Also, our findings are consistent with the resource-based view, transaction cost theory, and empirical evidence on the role of intellectual capital, human capital, and structural capital in the business success. In sum, based on these findings, it can be concluded that intellectual capital and its components (human and structural capital) play an important role by improving the performance in preventing the financial distress.
 


کلیدواژه‌ها [English]

  • Ensemble classifiers
  • Financial Distress
  • Intellectual capital
  • Tehran Stock Exchange
منابع
الف. فارسی
پورحیدری، امید و کوپایی‌حاجی، مهدی (1389). پیش‌بینی بحران‌مالی شرکت‌ها با استفاده از مدل مبتنی بر تابع تفکیکی خطی، پژوهش‌های حسابداری مالی، 2(1)، 46-33.
ستایش، محمدحسین و کاظم‌نژاد، مصطفی (1388). بررسی تأثیر سرمایة فکری بر عملکرد شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران. مجله پیشرفت‌های حسابداری، 1(1)،  94- 69.
ستایش، محمدحسین، کاظم‌نژاد، مصطفی، و حلاج، محمد (1395). بررسی سودمندی طبقه‌بندی‌کنندة جنگل‌های تصادفی و روش انتخاب متغیر ریلیف در پیش‌بینی بحران مالی: مطالعة شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران، پژوهش‌های حسابداری مالی، 8(2)، 24-1.
خواجوی، شکرالله و قدیریان‌آرانی، محمدحسین (1396). نقش توانایی مدیریت در پیش‌بینی بحران مالی، پژوهش‌های حسابداری مالی، 9(4)، 102-83.
خواجوی، شکرالله و قدیریان‌آرانی، محمدحسین (1397). توانایی مدیران، عملکرد مالی و خطر ورشکستگی، دانش حسابداری، 9(1)، 61-35.
فلاح‌پور، سعید و ارم، اصغر (1395). پیش‌بینی درماندگی مالی شرکت‌ها با استفاده از الگوریتم کلونی مورچگان، تحقیقات مالی، 18(2)، 368-347.
کرمی، غلامرضا و سیدحسینی، سید مصطفی (1391). سودمندی اطلاعات حسابداری نسبت به اطلاعات بازار در پیش‌بینی ورشکستگی، دانش حسابداری، 3(3)، 116-93.
گوارا، مریم، معین‌الدین، محمود، و عبقری، رامین (1395)، تأثیر کاهش داده‌ها با استفاده از تحلیل عاملی بر دقت مدل‌های پیش‌بینی ورشکستگی، پیشرفت‌های حسابداری، 8(2)، 189-151.
مرادی شهدادی، خسرو، انواری رستمی، علی اصغر، رنجبر، محمدحسین، و صادقی شریف، سید جلال (1396). تبیین نقش سرمایة فکری در کاهش احتمال ورشکستگی شرکت‌ها: شواهدی از بورس اوراق بهادار تهران، پژوهش‌های مدیریت منابع سازمانی، 7(4)، 178-156.
مشایخی، بیتا، و گنجی، حمیدرضا (1393). تأثیر کیفیت سود بر پیش‌بینی ورشکستگی با استفاده از شبکة عصبی مصنوعی، پژوهش‌های حسابداری مالی و حسابرسی، 6(2)، 173-147.
نیکبخت، محمدرضا و شریفی، مریم (1389). پیش‌بینی ورشکستگی مالی شرکت‌های بورس اوراق بهادار تهران با استفاده از شبکه‌های عصبی مصنوعی، مدیریت صنعتی، 2(4)،  180-163.
نمازی، محمد و ابراهیمی، شهلا (1388). بررسی تأثیر سرمایة فکری بر عملکرد جاری و آیندة شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران، تحقیقات حسابداری، 1(4)، 25-4.
نمازی، محمد و ابراهیمی، شهلا (1390)، بررسی تجربی نقش اجزای سرمایة فکری در ارزیابی عملکرد مالی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران، پیشرفت‌های حسابداری، 3(2)، 197-163.
نمازی، محمد و قدیریان‌آرانی، محمدحسین (1393). بررسی رابطة سرمایة فکری و اجزای آن با خطر ورشکستگی شرکت‌های پذیرفته‌شده در بورس اوراق بهادار تهران، پژوهش‌های تجربی حسابداری، 3(3)،  141-115.
 ب. انگلیسی
 Alfaro, E., García, N., Gámez, M., & Elizondo, D. (2008). Bankruptcy forecasting: An empirical comparison of AdaBoost and neural networks. Decision Support Systems, 45 (1), 110–122.
Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23 (4), 589-609.
Altman, E., & Hotchkiss, E. (2006). Corporate Financial Distress and Bankruptcy: Predict and Avoid Bankruptcy, Analyze and Invest in Distressed Debt. Hoboken, New Jersey: John Wiley & Sons, Inc.
Andrews, K. R. (1971). The Concept of Corporate Strategy. Berkeley California: Dow Jones-Irwin.
Argenti, J. (1976). Corporate Collapse: The Causes and Symptoms. New York: McGraw-Hill.
Bhunia, A., Khan, S. I. U., & Mukhuti, S. (2011). Prediction of financial distress- A case study of Indian companies. Asian Journal of Business Management, 3 (3), 210-218.
Bontis, N. (1998). Intellectual capital: An exploratory study that develops measures and models. Management Decision, 36 (2), 63-76.
Bontis, N., Keow, W. C., & Richardson, S. (2000). Intellectual capital and business performance in Malaysian industries. Journal of Intellectual Capital, 1 (1), 85-100.
Bruno, A. V., & Leidecker, J. K. (1988). Causes of new venture failure: 1960s vs. 1980s. Business Horizons, 31 (6), 51-56.
Campbell, J., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. Journal of Finance, 63 (6), 2899-2939.
Cao, Y., Wan, G., & Wang, F. (2011). Predicting financial distress of Chinese listed companies using rough set theory and support vector machine. Asia-Pacific Journal of Operational Research, 28 (1), 95-109.
Cenciarelli, V. G., Greco, G., & Allegrini, M. (2018). Does intellectual capital help predict bankruptcy? Journal of Intellectual Capital, 19(2), 321-337.
Chen, J., Zhu, Z., & Xie, Y. H. (2004). Measuring intellectual capital: A new model and empirical study. Journal of Intellectual Capital, 5 (1), 85-100.
Daubie, M., & Meskens, N. (2002). Business failure prediction: A review and analysis of the literature. In New trends in banking management (pp. 71-86). New York: Physica-Verlag.
Davalos, S., Leng, F., Feroz, E. H., & Cao, Z. (2009). Bankruptcy classification of firms investigated by the US Securities and Exchange Commission: An evolutionary adaptive ensemble model approach. International Journal of Applied Decision Sciences, 2 (4), 360-388.
Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10 (1), 167-179.
Dun & Bradstreet (1985). Business Failure Record, 1982–1983. New York: Dun & Bradstreet, 1985), pp. 14–15.
Etemadi, H., Anvary Rostamy, A. A., & Farajzadeh Dehkordi, H. (2009). A genetic programming model for bankruptcy prediction: Empirical evidence from Iran. Expert Systems with Applications, 36 (2), 3199-3207.
Fallahpour, S., Eram, A. (2016). Predicting Companies financial distress by using ant colony algorithm. Financial Research Journal, 18(2), 347-368. (In Persian)
Gavara, M., Moeinadin, M., Abghar, R. (2017). Data reduction influence on the accuracy of prediction failure company models. Journal oF Accounting Advances , 8(2), 151-189. (In Persian)
Gilbert, L. R., Menon, K., & Schwartz, K. B. (1990). Predicting bankruptcy for firms in financial distress. Journal of Business Finance & Accounting, 17 (1), 161-171.
Gitman, L. J. (1998), Principles of Managerial Finance. Boston: Addison Wesley.
Griffin, M., & Lemmon, J. M. (2002). Book-to-market equity, distress risk, and stock returns. Journal of Finance, 57 (5), 2317-2336.
Grunert, J., Norden, L., & Weber, M. (2005). The role of non-financial factors in internal credit ratings. Journal of Banking & Finance29(2), 509-531.
Holland, J. (2003). Intellectual capital and the capital market–organisation and competence. Accounting, Auditing & Accountability Journal, 16 (1), 39-48.
Hu, Y. C. (2010). Analytic network process for pattern classification problems using genetic algorithms. Information Sciences, 180, 2528–2539.
Hung, C., & Chen, J. H. (2009). A selective ensemble based on expected probabilities for bankruptcy prediction. Expert Systems with Applications, 36 (3), 5297–5303.
Kalay, A., Singhal, R., & Tashjian, E. (2007). Is chapter 11 costly? Journal of Financial Economic, 8 (3), 772-796.
Karami, G., Seyed Hosseini, S. (2012). Usefulness of accounting information vs. market information in bankruptcy Prediction.Journal of Accounting Knowledge , 3(10), 93-116. (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)
Khajavi, S., Ghadirian-Arani, M. (2018). The role of managerial ability in financial distress prediction. Journal of Financial Accounting Research, 9(4), 83-102. (In Persian)
Khajavi, S., Ghadirian-Arani, M. H., & Fattahi-Nafchi, H. (2016). Intellectual capital and earnings quality: A comprehensive investigation. International Journal of Learning and Intellectual Capital, 13(4), 316-337.
Lev, B. (2000). Intangibles: Management, Measurement, and Reporting. Washington DC: Brokings Instiution Press.
Lev, B., Radhakrishnan, S. & Zhang, W. (2009). Organization capital. Abacus, 45 (3), 275–298.
Leverty, T. & M. Grace, (2012). Dupes or incompetents? An examination of management’s impact on firm distress. The Journal of Risk Insurance, 79 (3), 751-783.
Lussier, R. N., & Corman, J. (2015). A business success versus failure prediction model for entrepreneurs with 0-10 employees. Journal of Small Business Strategy, 7 (1), 21-36.
Lussier, R. N., & Pfeifer, S. (2001). A crossnational prediction model for business success. Journal of Small Business Management, 39 (3), 228–239.
Madrid-Guijarro, A., García-Pérez-de-Lema, D. & Van Auken, H. (2011). An analysis of non-financial factors associated with financial distress. Entrepreneurship & Regional Development: An International Journal, 23 (3-4), 159-186.
Manzaneque, M., Merino, E., & Priego, A. M. (2016). The role of institutional shareholders as owners and directors and the financial distress likelihood. Evidence from a concentrated ownership context. European Management Journal34 (4), 439-451.
Marilenaa, M., & Alinaa, T. (2015). The significance of financial and non-financial information in insolvency risk detection. Procedia Economics and Finance, 26, 750-756.
Mashayekhi, B., Ganji, H. R. (2014). The impact of profit quality on bankruptcy prediction using artificial neural network, Financial and Auditing Accounting Research, 6 (2), 173-147. (In Persian)
Molodchik, M. A., Shakina, E. A., & Barajas, A. (2014). Metrics for the elements of intellectual capital in an economy driven by knowledge. Journal of Intellectual Capital, 15 (2), 206-226.
Montgomery, C. A., & Wernerfelt, B. (1988). Diversification, Ricardian rents, and Tobin’s q. Journal of Economics, 19 (4), 623-32.
Moradi Shahdadi, Kh., Anvari Rostami, A., Ranjbar, M. H., Sadeghi Sharif,  J. (2017). Explaining the role of intellectual capital in reducing the probability of bankruptcy of companies: evidence from the Tehran Stock Exchange, Organizational Resource Management Research, 7 (4), 178-156. (In Persian)
Namazi, M. Ebrahimi, Sh. (2009). Investigating the effect of intellectual capital on the current and future performance of companies listed on the Tehran Stock Exchange, Accounting Research, 1 (4), 25-4. (In Persian)
Namazi, M., Ebrahimi, S. (2012). Investigating the impact of the components of intellectual capital on the firm’s financial performance: evidence from Tehran Stock Exchange (TSE). Journal of Accounting Advances, 3(2), 163-197. (In Persian)
Namazi, M., Ghadiryian Arani, M. (2014). Investigation of the relationship between bankruptcy risk, intellectual capital and its components for the companies listed in Tehran Stock Exchange. Empirical Research in Accounting, 3(3), 115-141. (In Persian)
Nelson, R. R., & Sidney, G. Winter. (1982). An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, MA.
Nelson, R. R., & Winter, S. G. (1982). An evolutionary theory of economic change. Cambridge, MA: Harvard University Press.
Newton, G. W. (2010). Bankruptcy and Insolvency Accounting, Practice and Procedure. Vol. 1, 7th Ed. Hoboken, New Jersey: John Wiley & Sons, Inc.
Newton, G.W. (2010). Bankruptcy and Insolvency Accounting, Practice and Procedure, Vol. 1, 7th Ed, John Wiley & Sons, Inc., Hoboken, New Jersey.
Nikbakht, M. R., Sharifi, M. (1389). Predicting the financial bankruptcy of Tehran Stock Exchange companies using artificial neural networks, Industrial Management, 2 (4), 180-163. (In Persian)
Park, C-S., & I. Han (2002). A case-based reasoning with the feature weights derived by analytic hierarchy process for bankruptcy prediction. Expert Systems with Applications, 23 (3), 255–264.
Pervan, I., & Kuvek, T. (2013). The relative importance of financial ratios and nonfinancial variables in predicting of insolvency. Croatian Operational Research Review, 4(1): 187-198.
Peteraf, M. A. (1993). The cornerstones of competitive advantage: A resource-based view. Strategic Management Journal, 14(3), 179-88.
Pindado, J., Rodrigues, L., & De la Torre, C. (2008). Estimating financial distress likelihood. Journal of Business Research, 61(9), 995-1003.
Pourheydari, O., koopaee haji, M. (2010). predicting of firms financial distress by use of linear discriminant function the model. Journal of Financial Accounting Research, 2(1), 33-46. (In Persian)
Psillaki, M., Tsolas, I. E., & Margaritis, D. (2010). Evaluation of credit risk based on firm performance. European Journal of Operational Research, 201 (3), 873–881.
Rashid, A., & Abbas, Q. (2011). Predicting bankruptcy in Pakistan. Theoretical & Applied Economics, 18 (9), 103-128.
Rodov, I., & Leliaert, P. (2002). FiMIAN: Financial method of intangible assets measurement. Journal of Intellectual Capital, 3 (3), 323-336.
Sandin, A. R., & Porporato, M. (2008). Corporate bankruptcy prediction models applied to emerging economies: Evidence from Argentina in the years 1991-1998. International Journal of Commerce and Management, 17 (4), 295-311.
Setayesh, M., kazemnezhad, M., hallaj, M. (2016). The usefulness of random forest classifier and relief features selection in financial distress prediction: empirical evidence of companies listed on Tehran Stock Exchange. Journal of Financial Accounting Research, 8(2), 1-24. (In Persian)
Simnett, R., & Trotman, K. (1992). Identification of key financial ratios for going concern decisions. Charter, 39-41.
Stayesh, M. H., Kazemnejad, M. (2009). Investigating the effect of intellectual capital on the performance of companies listed on the Tehran Stock Exchange. Journal of Accounting Advances, 1 (1), 94-69. (In Persian)
Stewart, T. A. (1997). Intellectual Capital: The New Wealth of Organizations. Doubleday, New York, NY
Stewart, T. A. (1997). Intellectual Capital: The New Wealth of Organizations. New York, NY: Doubleday.
Sun, J., Jia, M., & 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.
Teece, D., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18 (7), 509-533.
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 (December), 394-419.
Venieris, G., Naoum, V-C., & Vlismas, O. (2015). Organization capital and sticky behaviour of selling, general and administrative expenses, Management Accounting Research, 26, 54-82.
Xie, C., Luo, C., & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies. Quality & Quantity,45(3), 671-686.
Yeh, C. C., Chi, D. J., & Lin, Y. R. (2014). Going-concern prediction using hybrid random forests and rough set approach. Information Sciences254 (January), 98-110.
Zavgren, C. V. (1985). Assessing the vulnerability to failure of American industrial firms: A logistic analysis. Journal of Business Finance and Accounting, 12 (1), 19-45.