سیاستگذاری سیستم‌های مالی در شرایط بحران با مدل‌سازی مبتنی بر شبکه‌های عصبی مصنوعی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد مهندسی صنایع، دانشگاه میبد، میبد، ایران.

2 استادیار، گروه مهندسی صنایع، دانشگاه میبد، میبد، ایران.

چکیده

امروزه سیاستگذاری در شرایط بحران مالی با هدف خنثی‌سازی تبعات سوء اقتصادی، اجتماعی و سیاسی به یکی از مهم‌ترین ارکان مدیریت اقتصاد جهانی تبدیل شده است. با عنایت به پیشرفت سریع فناوری و تکنولوژی‌ها‌ی کامپیوتری می‌توان الگوی دقیق تری از این پدیده بر اساس تجربیات قبلی ترسیم و در قالب یک سیستم پشتیبان تصمیم گیری مورد استفاده قرار داد. با تکیه بر قابلیت تعمیم‌دهی مدل‌های شبکه عصبی مصنوعی، این رویکرد به منظور مدل‌سازی دینامیک‌های موجود در پدیده بحران مالی مورد استفاده قرار گرفته است. متغیرهای وضعیت اقتصادی، تولید ناخالص داخلی، شاخص ارزش صادرات، شاخص ارزش واردات، موقعیت زمانی و موقعیت جغرافیایی هر کشور در هنگام وقوع بحران مالی به‌عنوان ورودی‌های مدل شبکه عصبی مصنوعی و ترکیب بهینه سیاست‌ها برای مقابله با بحران مالی به‌عنوان خروجی مدل تعریف شده است. به‌منظور آموزش این شبکه، از اطلاعات مشخصات و شرایط حاکم بر سیستم‌ها و نیز سیاست‌های اتخاذ شده در مواجهه با بحران‌های مالی بزرگ دنیا از سال 1997 تا به امروز استفاده شده است. به‌منظور نشان دادن قابلیت مدل پیشنهادی، نحوه طراحی و پیاده‌سازی سیستم پیشنهادی در مورد بحران شیوع ویروس کووید-19 در ایران مورد کاوی شد. نتایج به‌دست‌آمده بیانگر آن می‌باشد که استفاده از مدل پیشنهادی به‌عنوان پشتیبان سیاستگذاران و تصمیم‌گیران حوزه‌های مدیریت مالی می‌تواند در حل مسائل نیمه ساختار یافته کمک‌کننده باشد و موجب بهبود کارایی تصمیم‌گیری و توجه بیشتر به اثر بخشی آن شود. به‌طوری که با توجه به نتایج حاصل از پژوهش حاضر، اتخاذ سیاست‌های پولی و مالی انبساطی و اعطای بسته‌های حمایتی به‌عنوان راهکارهای اساسی جهت کاهش اثرات بحران مالی ناشی از همه‌گیری کرونا در کشور ایران توصیه می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Sustainable policy-making of financial systems in crisis situations with modelling based on artificial neural networks

نویسندگان [English]

  • Saba GhaziAskari 1
  • Najmeh Neshat 2
  • AbbasAli Jafari Nodoushan 2
1 M.A. Student in Industrial Engineering, Meybod University, Meybod, Iran
2 Assistant Prof., Department of Industrial Engineering, Meybod University, Meybod, Iran
چکیده [English]

Due to the rapid advancement of technology and computer technologies, a more accurate model of this phenomenon can be drawn based on previous experiences and used in the form of a decision support system. Relying on the generalizability of artificial neural network models, this approach has been used to model the dynamics of the financial crisis phenomenon. Variables of economic status, GDP, export value index, import value index, time position and geographical location of each country during the financial crisis as inputs of the artificial neural network model and the optimal combination of policies to deal with the financial crisis as Model output is defined. In order to show the capability of the proposed model, how to design and implement the proposed system in the event of a Covid-19 virus outbreak crisis in Iran was explored. The results indicate that using the proposed model as a support for policymakers and decision-makers in the field of financial management can help solve semi-structured problems and improve decision-making efficiency and pay more attention to its effectiveness. According to the results of the present study, the adoption of expansionary monetary and fiscal policies and the provision of support packages as basic solutions to reduce the effects of the financial crisis caused by the corona epidemic in Iran is recommended.

کلیدواژه‌ها [English]

  • financial crisis
  • policy
  • modelling
  • Artificial neural network

مراجع

  1. Abolhassan, H. A., & Shirazi, Z. (2011). The Economic and Social Crisis of Europe in the Third Millenninm: England, France and Germany. Journal of International Stadies, 4(17), 9-32. (In Persian)
  2. Anginer, D., Bertay, A., Cull, R., Demirgüç-Kunt, A., & Mare, D. S. (2021). Bank Capital Regulation and Risk after the Global Financial Crisis. Journal of Financial Stability, 59 100891.
  3. Bahriya, M., & Ghasemi, H. R. (2020). Consequences and Risk of Global Financial Reaction to Covid 19. Journal of Applied Studies in Management and Development Sciences, 6(1) 20-32. (In Persian).
  4. Bakhteyarzadeh, M. J. (2009). Investigating the Causes of the 2008 US Economic Crisis and Offering Solutions. Journal of Business Reviews, 7(38), 50-58. (In Persian).
  5. Bayani, A., Mohammadi, T., Bahrami, J., & Tavaklian, H. (2019). Quarterly Journal of Economic Modelling, 13(2), 45-72.
  6. Bhar, R., & Malliaris, A. G., (2020). Modeling U.S. monetary policy during the global financial crisis and lessons for Covid-19. Journal of Policy Modeling, 43(1), 15-33.
  7. Broadstock, D.; Kalok; CH, Louis, T. W.; & Wang, X. (2021). The Role of ESG Performance During Times of Financial Crisis: Evidence from COVID-19 in China. Finance Research Letters, 38, 101716.
  8. Claessens, S., & Kose, A. (2013). Financial Crises Explanations, Types, and Implications. International Monetary Fund. Washington, DC.
  9. Dolu, M., & Heydari, T. (2017). Predicting Stock Index Using a Combination of Artificial Neural Network and Meta-Innovative Models of Harmonic Search and Genetic Algorithm. Financial Economics, 11(1), 1-23. (In Persian).
  10. Giampaoli1, V., Karin, A., Norma, P. Luiz, S. (2016). Prediction of a Financial Crisis in Latin American Companies Using the Mixed Lgistic Regression model. Chilean Journal of Statistics, 7, 31-41.
  11. Gökçehan, H., & Waseem, A. (2014). Factors Affecting the Financial Performance of the Firms during the Financial Crisis: Evidence from Turkey. Ege Strategic Research Journal, 5(2), 65-80.
  12. Goodell, J. (2020). COVID-19 and Finance: Agendas for Future Research. Finance Research Letters, 35, 101512.
  13. Haghparast, A., Momeni, A., Gord, A., & Mansoori, F. (2021). Imaged Fnancial Ratios and Bankruptcy Prediction Using Convolutional Neural Networks. Financial Engineering and Portfolio Management, 46(1), 558-575. (In Persian).
  14. Irvani, M. J., (2010). The Global Financial Crisis and some Strategic Proposals. Journal of Business Management, 1(2), 33-46. (In Persian).
  15. Jena, P., Ritanjali, M., Rajesh, K., Shunsuke, M., & Babita, M. (2021). Impact of COVID-19 on GDP of Major Economies: Application of the Artificial Neural Network Forecaster. Economic Analysis and Policy, 69, 324-339.
  16. Jeong, G., & Kim, H. (2019). Improving Financial Trading Decisions Using Deep Q-Learning: Predicting the Number of Shares, Action Strategies, and Transfer Learning. Expert Systems with Applications, 117, 125-138.
  17. Kalkavan, H., & Ersin, I. (2019). Determination of Factors Affecting the South East Asian Crisis of 1997 Probit-Logit Panel Regression: The South East Asian Crisis. IGI Global, 7, 22-32.
  18. Kim, H., Batten, A., & Ryu, D., (2019). Financial Crisis, Bank Diversification, and Financial Stability: OECD countries. International Review of Economics & Finance, 65(1), 94-104.
  19. Mahmoudiazar, M., & Raei, R. (2014). Prediction of Stock Market Returns with out of Sample Data: Evaluating out of Sample Methods (Regression Method and Wavelet Neural Network). Assets Management and Financing, 2(2), 1-16. (In Persian).
  20. Malte, J. (2020). Artificial Neural Network Regression Models in a Panel Setting: Predicting Economic Growth. Economic Modelling, 2(11), 1-20.
  21. Mir Fakhraei, H. (2015). The EU and the Financial Crisis: Trends and Prospects. Journal of Political Science, 111(31), 189-223. (In Persian).
  22. Mirjalili, S. H., (2017). Critique and Analysis of the Effects of the Experience of East Asian Economies in Economic Resilience. Critical Studies in Texts & Programs of Human Sciences, 9(52), 211-231.
  23. Mirnezami, R., & Rajabi, S. (2020). Estimating the Impacts of COVID-19 on Iran Economy: Modelling Seven Scenarios. Journal of Science and Technology Policy, 10(2), 7-19. (In Persian).
  24. Mohebbi, S., Fadaienejad, M. E., & Hamidizadeh, M. R. (2021). The Proposed Algorithm to Select Appropriate Features for Predicting Tehran Stock Exchange Index. Financial Management Perspective, 34(11), 1-18. (In Persian).
  25. Mostafa Pour, M. (2009). The Effects of the Global Financial Crisis on the Economies of Asian Countries and the Economic Outlook of Asia during the Years (2010-2009). Review of Economic Issues and Policies, 9(95), 135-148. (In Persian).
  26. Najafi Estamal, S., Hosseini, SH., Memarnejad, A., & Ghaffari, F. (2020). Investigating the Effect of Financial Crisis Transfer Mechanism (with Emphasis on 2008 Financial Crisis and Oil Prices) and Markov Switching Causality on Selected Indices of Iran Stock Exchange. Journal of Financial Economics, 55,59-88.
  27. Namazi, M., Kazemnezhad, M., & Nematollahi, M. (2016). Comparing Different Feature Selection Methods in Financial Distress Prediction of the Firms Listed in Tehran Stock Exchange. Financial Engineering and Portfolio Management, 7(29), 193-212. (In Persian)
  28. Rostamnejad Nashli, A., Montazer Al-Qaim, A., & Fayyaz Anoush, A. (2016). A Study of Iran's Economic Policies and Challenges in the 1929 Economic Crisis; Documentary study. Document Treasure, 26(103), 8-38. (In Persian).
  29. Salehi, M., & Fakhri Pilerood, L. (2018). Predict of Profit Management Using Neural Network. Financial accounting and auditing, 10(37), 1-24. (In Persian).
  30. Saneifar, M., & Saeedi, P. (2020). Comparison of Complex Networks of Stock Markets and Economic Variables in the Period Before and After the Outbreak of Coronavirus (Covid-19). Journal of Economic Modeling Research, 10(40),123-145. (In Persian).
  31. Shakibaei, A., & Saeid, M. (2012). The Impact of the 2009-2007 Financial Crisis on the Trade Convergence of Developed Countries (Case Study: OECD). Journal of Economics and Logic Development, 19(4), 76-98. (In Persian).
  32. Trierweiler Ribeiro, G., Santos, A. P., Cocco, A., Viviana, M., & Leandro, D.S. (2021). Novel Hybrid Model Based on Echo State Neural Network Applied to the Prediction of Stock Price Return Volatility. Expert Systems with Applications, 184, 115490.
چشم انداز مدیریت مالی
دوره 12، شماره 38
تیر 1401
صفحه 103-129

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