پیش بینی ریسک نقدینگی با استفاده از تحلیل تمایلات خبری
محورهای موضوعی : فناوری اطلاعات و ارتباطات
حامد میرشک
1
(دانشجو)
امیر البدوی
2
(دانشگاه تربیت مدرس)
مهرداد کارگری
3
(استاد دانشگاه)
محمد علی رستگار
4
(استاد دانشگاه)
محمد طالبی
5
(دانشگاه امام صادق)
کلید واژه: پيشبيني ریسک نقدینگي, یادگيري ماشين, تحليل تمایل, تحليل سناریو, روششناسي علم طراحي,
چکیده مقاله :
یکي از م شکلات ا سا س ي بانکهاي ایراني نبود فرآیند مدیریت ری سک با رویکردي آیندهنگر ا ست . از مهمترین این ری سک ها در بانک، ميتوان به ری سک نقدینگي ا شاره کرد ؛ بنابراین پيشبيني ری سک نقدینگي به مو ضوع ي مهم براي بانکها تبدیل شده ا س ت. روشهاي مرسوم اندازه گيري ریسک نقدینگي پيچيده، زمانبر و پرهزینه هستند که پيشبيني آن را نيز غير قابل دسترس نموده اس ت. پيشبيني ریسک نقدینگي در زمان مناسب ميتواند از بروز مشکلات یا بحرانهاي جدي در بانک جلوگيري نماید. در این مطالعه سعي شده است تا راهحلي نوآورانه براي پيشبيني ریسک نقدینگي بانک و سناریوهاي پيشرو با استفاده از رویکرد تحليل تمایلات خبري ارائه شود . از رویکرد تحليل تمایل اخبار پيرامون یکي از بانکهاي ایراني در را ستاي شنا سایي متغيرهاي کيفي پ ویا و مؤثر در ریسککک نقدینگي بهره برده شده تا روشي سادهتر و با کارایي بالاتر براي پيشبيني روند ریسک نقدینگي ارائه نماید. روش پيشنهادي سناریوهاي عملي را براي تص ميمگيرندگان ریسک بانکي در دنياي واقعي فراهم ميکند. س کناریوهاي ریسک نقدینگي به د ست آمده در مقای سه با سنار یوهاي رخ داده در بانک طبق د ستورالعمل کم يته بازل و نظر کار شنا سان بانک ي ارزیابي مي شوند تا از صحت پ يشبينيها و همسو یي آن اطمينان حاصل شود. نتيجه ارزیابي سنار یوهاي موردمطالعه بهصورت دورهاي حاکي از دقت نسبتاً بالا است. معيار دقت 1 پيشبيني در سککناریوهاي محتمل اسککتاراش شده از کميته بازل، 95.5 % و در سککناریوهاي برگرفته از نظرات خبرگان، 75 % است
One of the main problems of Iranian banks is the lack of risk management process with a forward-looking approach, and one of the most important risks in banks is liquidity risk. Therefore, predicting liquidity risk has become an important issue for banks. Conventional methods of measuring liquidity risk are complex, time-consuming and expensive, which makes its prediction far from possible. Predicting liquidity risk at the right time can prevent serious problems or crises in the bank. In this study, it has been tried to provide an innovative solution for predicting bank liquidity risk and leading scenarios by using the approach of news sentiment analysis. The news sentiment analysis approach about one of the Iranian banks has been used in order to identify dynamic and effective qualitative factors in liquidity risk to provide a simpler and more efficient method for predicting the liquidity risk trend. The proposed method provides practical scenarios for real-world banking risk decision makers. The obtained liquidity risk scenarios are evaluated in comparison with the scenarios occurring in the bank according to the guidelines of the Basel Committee and the opinion of banking experts to ensure the correctness of the predictions and its alignment. The result of periodically evaluating the studied scenarios indicates a relatively high accuracy. The accuracy of prediction in possible scenarios derived from the Basel Committee is 95.5% and in scenarios derived from experts' opinions, 75%.
] اصغر ب. استادی, پ. ت. پژو و ه. اشعری, “ارزيابي و ارايه الگوي مناسب براي شناسايي، اندازه گيري و كنترل ريسكهاي مالي در مؤسسات مالي و اعتباري با استفاده از روش ضریب تغییرات (CV),” تیر 95.
[2] F. T. Musakwa, "On measuring bank funding liquidity risk," Actuarial Society of South Africa’s 2013 Convention, pp. 227-247, 2013.
[3] P. G. G.A. Vento, "Bank Liquidity Risk Management and Supervision: Which Lessons from Recent Market Turmoil?," Journal of Money, Investment and Banking, no. 10, pp. 78-125, 2009.
[4] A. A. D. D. C. M. P. M. Tavanaa, "An Artificial Neural Network and Bayesian Network model for liquidity risk assessment in banking," Neurocomputing, vol. 275, pp. 2525-2554, 2018.
[5] L. Matz, "Scenario analysis and stress testing," in Liquidity Risk Measurement and Management, New Jersey, John Wiley & Sons. Inc, 2007, pp. 37-64.
[6] P. G. R. Calabrese, "Estimating bank default with generalised extreme value regression models," The Journal of the Operational Research Society, vol. 66, no. 11, pp. 1783-1792, 2015.
[7] J. M. H. &. Z. X. Bollen, "The Review of Financial Studies," Horizon effects in average returns: The role of slow information diffusion, vol. 29, pp. 2241-2281, 2016.
[8] R. S. J.Y. Campbell, "Cointegration and tests of present value models," Journal of Political Economy, vol. 95, pp. 1062-1088, 1987.
[9] M. L. M. &. N. D. Hagenau, "Automated news reading: Stock price prediction based on financial news using context-capturing features.," Decision Support Systems, vol. 55, pp. 685-697, 2013.
[10] H. C. R.P. Schumaker, "Textual analysis of stock market prediction using breaking financial news: the AZFin text system," ACM Transactions on Information Systems, vol. 27, no. 2, 2009.
[11] K. &. S. N. Nam, "Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market," Decision Support Systems, vol. 117, pp. 100-112, 2019.
[12] T. H. S. K. &. V. J. Nguyen, "Sentiment analysis on social media for stock movement," Expert Systems with Applications, vol. 42, pp. 9603-9611, 2015.
[13] M. &. F. S. Kraus, "Decision support from financial disclosures with deep neural networks and transfer learning," Decision Support Systems, vol. 104, pp. 38-48, 2017.
[14] M.-A. Mittermayr, "Forecasting intraday stock price trends with text mining techniques," Proceedings of the 37th Annual Hawaii International Conference on System Sciences, 2004.
[15] H. C. R.P. Schumaker, "Textual analysis of stock market prediction using breaking: the AZFin text system," ACM Transactions on Information Systems, vol. 2, p. 27, 2009.
[16] D. R. D. Robert A.Jarrowab, "The arbitrage-free valuation and hedging of demand deposits and credit card loans," Journal of Banking and Finance, vol. 22, no. 3, pp. 249-272, 1998.
[17] M. Kumar and G. C. Yadav, "Liquidity Risk Management in Bank ,A Conceptual Framework," AIMA Journal of Management & Research, 2013.
[18] B. C. o. B. Supervision, "Basel III: International framework for liquidity risk measurement, standards and monitoring," BIS.org, Switzerland, 2014.
[19] F. Musakwa, "Measuring Bank Funding Liquidity Risk," 2013. [Online]. Available: https://www.actuaries.org/lyon2013/papers/AFIR_Musakwa.pdf.
[20] B. C. O. B. Supervision, "Principles for Sound Liquidity Risk Management and Supervision," Bank for International Settlements, 2008.
[21] Neurocomputing, "Neural networks: An overview of early research, current frameworks and new challenges," Alberto Prieto,Beatriz Prieto, Eva Martinez, Ortigosa Eduardo, Ros Francisco, Pelayo Julio,OrtegaIgnacio Rojas, vol. 214, pp. 242-268, 2016.
[22] L. H. Z. G. Lian Duan, "Global robust dissipativity of interval recurrent neural networks with time-varying delay and discontinuous activations," Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 26, no. 7, pp. 073101-12, 2016.
[23] J. C. X. A. A. T. H. Chengdai Huang, "Bifurcations in a delayed fractional complex-valued neural network," Applied Mathematics and Computation, vol. 292, pp. 210-227, 2017.
[24] Z. W. X. L. N. Z. Y. L. F. E. Weibo Liu, "A survey of deep neural network architectures and their applications," Neurocomputing, vol. 234, pp. 11-26, 2017.
[25] K. Akyol, "Comparing of deep neural networks and extreme learning machines based on growing and pruning approach," Expert Systems with Applications, vol. 140, p. 112875, 2020.
[26] X. L. Yihao Zhang, "Learning attention embeddings based on memory networks for neural collaborative recommendation," Expert Systems with Applications , vol. 183, 2021.
[27] I. S. Sara Kavian, "Application of complex systems topologies in artificial neural networks optimization: An overview," Expert Systems with Applications, vol. 180, p. 115073, 2021.
[28] K. C. Ankit Thakkar, "A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions," Expert Systems With Applications , vol. 177, no. 114800, 2021.
[29] D. V. Shayanfar Nima, "An Intelligent News Based Decision Support System for Trading Stocks in Tehran Stock Exchange," Journal of Soft Computing and Information Technology, vol. 4, no. 3, pp. 55-61, 2014.
[30] A. H. Clemens Nopp, "Detecting Risks in the Banking System by Sentiment Analysis," in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, 2015.
[31] P. P. A. M. A. A. Hai Ha Do, "DEEP LEARNING FOR ASPECT-BASED SENTIMENT ANALYSIS: A COMPARATIVE REVIEW," Expert Systems With Applications, vol. 118, pp. 272-299, 2019.
[32] A. Paraboni, M. RighX, K. Vieira and V. da Silveira, "The Relationship between Sentiment and Risk in Financial Markets," BAR- Brazilian Administration Review, 2018.
[33] D. Chong, L. Li, H. Wu, J. Park, H. Shi and G. Yan, "Social Media Sentiment and Bank Loan," Journal of Industrial Integration and Management, 2018.
[34] W. He, S. Zha and L. Li, "Social media competitive analysis and text mining: A case study," International Journal of Information Management, p. 464–472, 2013.
[35] E. Altman, "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy," The journal of finance, p. 589–609, 1968.
[36] R. Nyman, "Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis," Centre for the Study of Decision Making Uncertainty, University College, London, 2015.
[37] C. E.-S. Yu, "Sentiment Analysis for Credit Risk and Portfolio Construction," 2020.
[38] M. &. Y. G. C. Kumar, "Liquidity Risk Management in Bank,A Conceptual Framework," AIMA Journal of Management & Research, 2013.
[39] C. Erlwein-Sayer, "Macroeconomic News Sentiment: Enhanced Risk Assessment for Sovereign Bonds," MDPI RISK, p. 141, 2018.
[40] A. H. A. M. Jan vom Brocke, "Introduction to Design Science Research," Springer International Publishing, pp. 1-13, 2020.
[41] A. M. S. P. J. &. R. S. Hevner, " Design Science in Information Systems," MIS Quarterly, vol. 28, no. 1, pp. 75-105, 2004.