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Machine learning methods for systemic risk analysis in financial sectors

    Gang Kou Affiliation
    ; Xiangrui Chao Affiliation
    ; Yi Peng Affiliation
    ; Fawaz E. Alsaadi Affiliation
    ; Enrique Herrera-Viedma Affiliation

Abstract

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.


First published online 29 May 2019

Keyword : financial systemic risk, machine learning, big data, network analysis

How to Cite
Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716-742. https://doi.org/10.3846/tede.2019.8740
Published in Issue
May 29, 2019
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Abedifar, P., Giudici, P., & Hashem, S. Q. (2017). Heterogeneous market structure and systemic risk: evidence from dual banking systems. Journal of Financial Stability, 33, 96-119. https://doi.org/10.1016/j.jfs.2017.11.002

Acemoglu, D., Ozdaglar, A., & Tahbaz-Salehi, A. (2015). Systemic risk and stability in financial networks. American Economic Review, 105(2), 564-608. https://doi.org/10.1257/aer.20130456

Acharya, V. V. (2009). A theory of systemic risk and design of prudential bank regulation. Journal of Financial Stability, 5(3), 224-255. https://doi.org/10.1016/j.jfs.2009.02.001

Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2010). Measuring systemic risk (Working paper). Federal Reserve of Cleveland.

Acharya, V., Engle, R., & Richardson, M. (2012). Capital shortfall: a new approach to ranking and regulating systemic risks. American Economic Review: Papers & Proceedings, 102(3), 59-64. https://doi.org/10.1257/aer.102.3.59

Adrian, T., Covitz, D., & Liang, N. (2015). Financial stability monitoring. Annual Review of Financial Economics, 7(1), 357-395. https://doi.org/10.1146/annurev-financial-111914-042008

Alexander, K. (2011). Reforming European financial supervision: adapting EU institutions to market structures. ERA Forum, 12(2), 229-252. https://doi.org/10.1007/s12027-011-0216-x

Allen, F., & Gale, D. (2000). Financial contagion. Journal of Political Economy, 108(1), 1-33. https://doi.org/10.1086/262109

Allen, F., Goldstein, I., Jagtiani, J., & Lang, W. W. (2016). Enhancing prudential standards in financial regulations. Journal of Financial Services Research, 49(2-3), 133-149. https://doi.org/10.1007/s10693-016-0253-2

Amini, H., Cont, R., & Minca, A. (2013). Resilience to contagion in financial networks. Mathematical Finance, 26(2), 329-365.

Fardani Haryadi, A., Hulstijn, J., Wahyudi, A., van der Voort, H., & Janssen, M. (2016). Antecedents of big data quality: An empirical examination in financial service organizations. In Proceedings of the IEEE International Conference on Big Data (pp. 116-121). https://doi.org/10.1109/BigData.2016.7840595

Arthur, K. N. A. (2017). Financial innovation and its governance: Cases of two major innovations in the financial sector. Financial Innovation, 3(1), 10. https://doi.org/10.1186/s40854-017-0060-2

Agliardi, R. (2018). Value-at-risk under ambiguity aversion. Financial Innovation, 4(1), 10. https://doi.org/10.1186/s40854-018-0095-z

Arora, D., & Rathinam, F. (2011). OTC derivatives market in India: recent regulatory initiatives and open issues for market stability and development. Macroeconomics and Finance Emerging Market Economies, 4(2), 235-261. https://doi.org/10.1080/17520843.2011.580571

Ashraf, D., Rizwan, M. S., & L’Huillier, B. (2016). A net stable funding ratio for Islamic banks and its impact on financial stability: an international investigation. Journal of Financial Stability, 25 (1), 47-57. https://doi.org/10.1016/j.jfs.2016.06.010

Aven, T. (2016). Risk assessment and risk management: review of recent advances on their foundation. European Journal of Operational Research, 253(1), 1-13. https://doi.org/10.1016/j.ejor.2015.12.023

Balogh, P. (2012). Macro prudential supervision tools in the European banking system. Procedia Economics and Finance, 3(1), 642-647. https://doi.org/10.1016/S2212-5671(12)00208-0

Bargigli, L., Di Iasio, G., Infante, L., Lillo, F., & Pierobon, F. (2014). The multiplex structure of interbank networks. Quantitative Finance, 15(4), 673-691.

Battaglia, F., & Gallo, A. (2017). Strong boards, ownership concentration and EU banks’ systemic risktaking: Evidence from the financial crisis. Journal of International Financial Markets, Institutions and Money, 46, 128-146. https://doi.org/10.1016/j.intfin.2016.08.002

Battiston, S., Caldarelli, G., May, R. M., Roukny, T., & Stiglitz, J. E. (2016). The price of complexity in financial networks. Proceedings of the National Academy of Sciences of the United States of America, 113(36), 10-31. https://doi.org/10.1073/pnas.1521573113

Battiston, S., Farmer, J. D., & Flache A. (2016). Complexity theory and financial regulation. Science, 351(6275), 818-819. https://doi.org/10.1126/science.aad0299

Bengtsson, E. (2014). Fund management and systemic risk – lessons from the global financial crisis. Financial Markets, Institutions & Instruments, 23(2), 101-124. https://doi.org/10.1111/fmii.12016

Bența, D., Rusu, L., & Manolescu, M. J. (2017). Workflow automation in a risk management framework for pavement maintenance projects. International Journal of Computers Communications & Control, 12(2), 155-165. https://doi.org/10.15837/ijccc.2017.2.2875

Bernardi, E., & Romagnoli, S. (2016). Distorted copula-based probability distribution of a counting hierarchical variable: a credit risk application. International Journal of Information Technology & Decision Making, 15(02), 285-310. https://doi.org/10.1142/S021962201650005X

Bernanke, B. S. (2009). The crisis and policy response, stamp lecture. London School of Economics.

Betz, F., Hautsch, N., Peltonen T. A., & Schienle, M. (2016). Systemic risk spillovers in the European banking and sovereign network. Journal of Financial Stability, 25, 206-224. https://doi.org/10.1016/j.jfs.2015.10.006

Billio, M., Getmansky, M., Lo, A. W., & Pelizzon, L. (2012). Econometric measures of connectedness and systemic risk in the finance and insurance sectors. Journal of Financial Economics, 104(3), 535-559. https://doi.org/10.1016/j.jfineco.2011.12.010

Blancher, N., Mitra, S., Morsy, H., Otani, A., Severo, T., & Valderrama, L. (2013). Systemic risk monitoring toolkit- a user guide (IMF working paper). https://doi.org/10.5089/9781484383438.001

Bluhm, M., & Krahnen, J. (2014). Systemic risk in an interconnected banking system with endogenous asset markets. Journal of Financial Stability, 13(1), 75-94. https://doi.org/10.1016/j.jfs.2014.04.002

Bongini, P., Nieri, L., Pelagatti, M., & Piccini, A. (2017). Curbing systemic risk in the insurance sector: A mission impossible? The British Accounting Review, 49(2), 2256-273. https://doi.org/10.1016/j.bar.2016.08.002

Borio, C. (2003). Towards a macroprudential framework for financial supervision and regulation? BIS Working Papers 1(128), 1-26.

Bosma, J. J. (2016). Dueling policies: why systemic risk taxation can fail. European Economic Review, 87(1), 132-147. https://doi.org/10.1016/j.euroecorev.2016.05.002

Bottani, E., Bertolini, M., Montanari, R., & Volpi, A. (2009). RFID-enabled business intelligence modules for supply chain optimisation. International Journal of RF Technologies: Research and Applications, 1(4), 253-278. https://doi.org/10.1080/17545730903321683

Brammertz, W., & Mendelowitz, A. I. (2014). Limits and opportunities of big data for macro-prudential modeling of financial systemic risk. International Workshop on Data Science for Macro-Modeling (pp. 1-6). ACM. https://doi.org/10.1145/2630729.2630741

Brownlees, C. T., & Engle, R. F. (2011). Volatility, correlation and tails for systemic risk measurement (Technical report). New York University. https://doi.org/10.2139/ssrn.1611229

Brownlees, C., & Engle, R. F. (2017). Srisk: a conditional capital shortfall measure of systemic risk (Working paper No. 37). European systemic risk board. https://doi.org/10.1093/rfs/hhw060

Brunnermeier, M. K., & Pedersen, L. H. (2009). Market liquidity and funding liquidity. The Review of Financial Studies, 22(6), 2201-2238. https://doi.org/10.1093/rfs/hhn098

Brunnermeier, M. K., & Sannikov, Y. (2014). A macroeconomic model with a financial sector. American Economic Review, 104(2), 379-421. https://doi.org/10.1257/aer.104.2.379

Calabrese, R., & Giudici, P. (2015). Estimating bank default with generalised extreme value regression models. Journal of the Operational Research Society, 66(11), 1783-1792. https://doi.org/10.1057/jors.2014.106

Calabrese, R., Elkink, J., & Giudici, P. (2017). Measuring bank contagion using binary spatial regression models. Journal of the Operational Research Society, 68(12), 1503-1511. https://doi.org/10.1057/s41274-017-0189-4

Calistru, R. A. (2012). The credit derivatives market – a threat to financial stability? Procedia – Social and Behavioral Sciences, 58(1), 552-559. https://doi.org/10.1016/j.sbspro.2012.09.1032

Calmès, C., & Théoret, R. (2013). Market-oriented banking, financial stability and macro-prudential indicators of leverage. Journal of International Financial Markets, Institutions and Money, 27 (1), 13-34. https://doi.org/10.1016/j.intfin.2013.07.004

Calmès, C., & Théoret, R. (2014). Bank systemic risk and macroeconomic shocks: Canadian and U.S. evidence. Journal of Banking & Finance, 40(1), 388-402. https://doi.org/10.1016/j.jbankfin.2013.11.039

Campbellverduyn, M., Goguen, M., & Porter, T. (2017). Big data and algorithmic governance: the case of financial practices. New Political Economy, 22(2), 219-236. https://doi.org/10.1080/13563467.2016.1216533

Cao, J., & Illing, G. (2010). Regulation of systemic liquidity risk. Financial Markets and Portfolio Management, 24(1), 31-48. https://doi.org/10.1007/s11408-009-0126-x

Carmassi, J., & Herring, R. (2016). The corporate complexity of global systemically important banks. Journal of Financial Services Research, 492, 175-201. https://doi.org/10.1007/s10693-016-0251-4

Cerchiello, P., & Giudici, P. (2015). Conditional graphical models for systemic risk estimation. Expert Systems with Applications, 43, 165-174. https://doi.org/10.1016/j.eswa.2015.08.047

Cerchiello, P., & Giudici, P. (2016). Big data analysis for financial risk management. Journal of Big Data, 3, 18. https://doi.org/10.1186/s40537-016-0053-4

Cerchiello, P., Giudici, P., & Nicola, D. (2016). Big data models of bank risk contagion (DEM Working Paper Series No. 117 (02-16)). Retrieved from http://epmq.unipv.eu/site/home.html

Chang, E., Guerra, S., Lima, E., & Tabak, B. (2008). The stability-concentration relationship in the Brazilian banking system. Journal of International Financial Markets, Institutions and Money, 18(4), 388-397. https://doi.org/10.1016/j.intfin.2007.04.004

Chao, X., & Peng, Y. (2017). A cost-sensitive multi-criteria quadratic programming model for imbalanced data. Journal of the Operational Research Society, (1), 1-17. https://doi.org/10.1057/s41274-017-0233-4

Chao, X., Kou, G., Li, T., & Peng, Y. (2018). Jie Ke versus AlphaGo: A ranking approach using decision making method for large-scale data with incomplete information. European Journal of Operational Research, 265(1), 239-247. https://doi.org/10.1016/j.ejor.2017.07.030

Chen, R., Wang, Z., & Yu, L. (2017). Importance sampling for credit portfolio risk with risk factors having t-copula. International Journal of Information Technology and Decision Making, 16(4), 1101-1124. https://doi.org/10.1142/S0219622017500201

Chiang, J. K., & Chen, C. C. (2015). Sentimental analysis on Big Data – on case of financial document text mining to predict sub-index trend. In 5th International Conference on Computer Sciences and Automation Engineering (ICCSAE 2015) (pp. 423-428). Sanya, China.

Chinazzi, M., & Fagiolo, G. (2015). Systemic risk, contagion, and financial networks: a survey (Working Paper Series (2013/08). Institute of Economics, Scuola Superiore Sant’Anna, Laboratory of Economics and Management (LEM). https://doi.org/10.2139/ssrn.2243504

Choi, D. (2014). Heterogeneity and stability: bolster the strong, not the weak. The Review of Financial Studies, 27(6), 1830-1867. https://doi.org/10.1093/rfs/hhu023

Choi, T. M., Chan, H. K., & Yue, X. (2017). Recent development in big data analytics for business operations and risk management. IEEE Transactions on Cybernetics, 47(1), 81-92. https://doi.org/10.1109/TCYB.2015.2507599

Clark, E., & Jokung, O. (2015). The role of regulatory credibility in effective bank regulation. Journal of Banking & Finance, 50(1), 506-513. https://doi.org/10.1016/j.jbankfin.2014.03.018

Cox, R., & Wang, G.-Y. (2014). Predicting the US bank failure: a discriminant analysis. Economic Analysis and Policy, 44(2), 202-211. https://doi.org/10.1016/j.eap.2014.06.002

Crăciun, M., Bucerzan, D., Raţiu, C., & Manolescu, A. (2013). Actuality of bankruptcy prediction models used in decision support system. International Journal of Computers Communications & Control, 8(3), 375-383. https://doi.org/10.15837/ijccc.2013.3.464

Cruz, J. P., & Lind, P. G. (2012). The dynamics of financial stability in complex networks. The European Physical Journal B, 85(8), 256. https://doi.org/10.1140/epjb/e2012-20984-6

Cui, D. M. (2015). Financial credit risk warning based on Big Data Analysis. Metallurgical & Mining Industry, 7(6), 133-141.

Diebold, F. X., & Yılmaz, K. (2014). On the network topology of variance decompositions: measuring the connectedness of financial firms. Journal of Econometrics, 182(1), 119-134. https://doi.org/10.1016/j.jeconom.2014.04.012

Dong, T., Yang, B., & Tian, T. (2015). Volatility analysis of Chinese stock market using high-frequency financial Big Data. In IEEE International Conference on Smart City/socialcom/sustaincom (pp. 769-774). IEEE Computer Society. https://doi.org/10.1109/SmartCity.2015.234

Dong, Y., Zha, Q., Zhang, H., Kou, G., Fujita, H., Chiclana, F., & Herrera-Viedma, E. (2018a). Consensus reaching in social network group decision making: Research paradigms and challenges. Knowledge-Based Systems, 162, 3-13. https://doi.org/10.1016/j.knosys.2018.06.036

Dong, Y., Zhan, M., Kou, G., Ding, Z., & Liang, H. (2018b). A survey on the fusion process in opinion dynamics. Information Fusion, 43, 57-65. https://doi.org/10.1016/j.inffus.2017.11.009

Duca, M., & Peltonen, T. (2013). Assessing systemic risks and predicting systemic events. Journal of Banking & Finance, 37(7), 2183-2195. https://doi.org/10.1016/j.jbankfin.2012.06.010

ECB. (2010). Analytical models and tools for the identification and assessment of systemic risks. In Financial Stability Review (June 2010). European Central Bank, Frankfurt.

Eisenberg, L., & Noe, T. H. (2001). Systemic Risk in Financial Systems. Management Science, 47(2), 236-249. https://doi.org/10.1287/mnsc.47.2.236.9835

Elliott, M., Golub, B., & Jackson, M. (2014). Financial networks and contagion. American Economic Review, 104(1), 3115-3153. https://doi.org/10.1257/aer.104.10.3115

Elsinger, H., Lehar, A., & Summer, M. (2006). Risk assessment for banking systems. Management Science, 52(9), 1301-1314. https://doi.org/10.1287/mnsc.1060.0531

Ellis, L., Haldane, A., & Moshirian, F. (2014). Systemic risk, governance and global financial stability. Journal of Banking & Finance, 45(1), 175-181. https://doi.org/10.1016/j.jbankfin.2014.04.012

Fazio, D. M., Tabak, B. M., Cajueiro, D. O. (2015). Inflation targeting: is it to blame for banking system instability? Journal of Banking Finance, 59(1), 76-97. https://doi.org/10.1016/j.jbankfin.2015.05.016

Feldman, R. (2013). Techniques and applications for sentiment analysis. Communications of the ACM, 56(4), 82-89. https://doi.org/10.1145/2436256.2436274

Fernández, A. I., González, F., & Suárez, N. (2016). Banking stability, competition, and economic volatility. Journal of Financial Stability, 22, 101-120. https://doi.org/10.1016/j.jfs.2016.01.005

Ferrara, G., Langfield, S., Liu, Z., & Ota, T. (2016). Systemic illiquidity in the interbank network (Staff Working Paper No. 586). Bank of England. https://doi.org/10.15847/CIESOEMWP022016

Flood, M., Jagadish, H. V., Kyle, A., Olken, F., & Raschid, L. (2011). Using data for systemic fiancial risk management. Paper presented at Proceedings of 5th Biennial Conference on Innovative Data Systems Research, Asilomar, CA, USA.

Freixas, X., Parigi, B. M., & Rochet, J. C. (2000). Systemic risk, interbank relations, and liquidity provision by the central bank. Journal of Money, Credit and Banking, 32(3), 611-638. https://doi.org/10.2307/2601198

Fukuyama, H., & Weber, W. L. (2015). Network performance of Japanese credit cooperatives, 2004–2007. International Journal of Information Technology & Decision Making, 14(4), 825-846. https://doi.org/10.1142/S0219622014500904

Gabbi, G., Iori, G., Jafarey, S., & Porter, J. (2014). Financial regulations and bank credit to the real economy. Journal of Economic Dynamics and Control, 50(1), 117-143.

Gaffeo, E., & Molinari, M. (2016). Macroprudential consolidation policy in interbank networks. Journal of Evolutionary Economics, 26(1), 77-99. https://doi.org/10.1007/s00191-015-0419-3

Galat, G., & Moessner, R. (2013). Macro prudential Policy: a literature review. Journal of Economic Surveys, 27(5), 846-878.

García, D. (2013). Sentiment during recessions. The Journal of Finance, 68(3), 1267-1300. https://doi.org/10.1111/jofi.12027

Gauthier, C., Lehar, A., & Souissi, M. (2012). Macroprudential capital requirements and systemic risk. Journal of Financial Intermediation, 21(4), 594-618. https://doi.org/10.1016/j.jfi.2012.01.005

Giudici, P., & Parisi, L. (2017). Sovereign risk in the Euro area: a multivariate stochastic process approach. Quantitative Finance, 17(12), 1995-2008. https://doi.org/10.1080/14697688.2017.1357968

Giudici, P., & Spelta, A. (2016). Graphical network models for international financial flows. Journal of Business & Economic Statistics, 34(1), 128-138. https://doi.org/10.1080/07350015.2015.1017643

Giudici, P., Cerchiello, P., & Nicola, G. (2016). Twitter data models for bank risk contagion. Neurocomputing, 264, 50-56. https://doi.org/10.1016/j.neucom.2016.10.101

Giudici, P., Sarlin, P., & Spelta, A. (2017). The interconnected nature of financial systems: direct and common exposures. Journal of Banking & Finance (In Press). https://doi.org/10.1016/j.jbankfin.2017.05.010

Haldane, A. G. (2015). On microscopes and telescopes. Paper presented at Workshop on Socio-Economic Complexity, 23-27 March 2015, Lorentz Center, Leiden. (Bank of England, London). Retrieved from http://bit.ly/1VIJlvX

Haldane, A., & May, R. (2011). Systemic risk in banking ecosystems. Nature, 469(7330), 351-355. https://doi.org/10.1038/nature09659

Hamdi, H., Hakimi, A., & Zaghdoudi, K. (2017). Diversification, bank performance and risk: have tunisian banks adopted the new business model? Financial Innovation, 3(1), 22. https://doi.org/10.1186/s40854-017-0069-6

Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497, 52-59. https://doi.org/10.1038/nature12047

Helwege, J., & Zhang, G. Y. (2016). Financial firm bankruptcy and contagion. Review of Finance, 20(4), 1321-1362. https://doi.org/10.1093/rof/rfv045

Hippler, W. J., & Hassan, M. K. (2015). The impact of macroeconomic and financial stress on the U.S. financial sector. Journal of Financial Stability, 21(1), 61-80. https://doi.org/10.1016/j.jfs.2015.09.008

Hu, D., Schwabe, G., & Li, X. (2015). Systemic risk management and investment analysis with financial network analytics: research opportunities and challenges. Financial Innovation, 1, https://doi.org/10.1186/s40854-015-0001-x

Hu, D., Zhao, J. L., Hua, Z., & Wong, M. C. S. (2012). Network based modeling and analysis of systemic risk in banking systems. MIS Quarterly, 36(4), 1269-1291. https://doi.org/10.2307/41703507

Huang, X., Zhou, H., & Zhu, H. (2012). Systemic risk contributions. Journal of Financial Services Research, 42(1-2), 55-83. https://doi.org/10.1007/s10693-011-0117-8

Huang, Y., & Kou, G. (2014). A kernel entropy manifold learning approach for financial data analysis. Decision Support Systems, 64, 31-42. https://doi.org/10.1016/j.dss.2014.04.004

Huang, Y., Kou, G., & Peng, Y. (2017). Nonlinear manifold learning for early warnings in financial markets. European Journal of Operational Research, 258(2), 692-702. https://doi.org/10.1016/j.ejor.2016.08.058

Huang, W. Q., Zhuang, X. T., Yao, S., & Uryasev, S. (2016). A financial network perspective of financial institutions’ systemic risk contributions. Physica A: Statistical Mechanics and its Applications, 456(1), 183-196. https://doi.org/10.1016/j.physa.2016.03.034

Hutchison, M. (2002). European banking distress and EMU: Institutional and macroeconomic risks. The Scandinavian Journal of Economics, 104(3), 365-389. https://doi.org/10.1111/1467-9442.00292

Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y., Patel, J. M., & Ramakrishnan, R., et al. (2014). Big data and its technical challenges. Communications of the ACM, 57(7), 86-94. https://doi.org/10.1145/2611567

Jin, X., & Nadal De Simone, F. (2014). A framework for tracking changes in the intensity of investment funds’ systemic risk. Journal of Empirical Finance, 29(1), 343-368. https://doi.org/10.1016/j.jempfin.2014.09.002

Jobst, A., & Gray, D. F. (2013). Systemic contingent claims analysis – estimating market-implied systemic risk. IMF Working Paper No. 13/54. https://doi.org/10.5089/9781475572780.001

Kara, G. I. (2016). Systemic risk, international regulation, and the limits of coordination. Journal of International Economics, 99(1), 192-222. https://doi.org/10.1016/j.jinteco.2015.11.007

Keynes, J. M. (1936). The General theory of employment, interest, and money. Macmillan and Co. Limited St. Martin’s street, London.

Khraisha, T., & Arthur, K. (2018). Can we have a general theory of financial innovation processes? A conceptual review. Financial Innovation, 4(1), 4. https://doi.org/10.1186/s40854-018-0088-y

Kim, K., Chung, B.-S., Jung, J.-Y., & Park, J. (2013). Revenue maximizing item set construction for online shopping services. Industrial Management & Data Systems, 113(1), 96-116. https://doi.org/10.1108/02635571311289683

King, M., & Maier, P. (2009). Hedge funds and financial stability: regulating prime brokers will mitigate systemic risks. Journal of Financial Stability, 5(3), 283-297. https://doi.org/10.1016/j.jfs.2009.02.002

Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. https://doi.org/10.1016/j.ins.2014.02.137

Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225. https://doi.org/10.1142/S0219622012500095

Kou, G., Peng, Y., & Wang, G. (2014). Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Information Sciences, 275, 1-12. https://doi.org/10.1016/j.ins.2014.02.137

Kou, G., Lu, Y., Peng, Y., & Shi, Y. (2012). Evaluation of classification algorithms using MCDM and rank correlation. International Journal of Information Technology & Decision Making, 11(01), 197-225. https://doi.org/10.1142/S0219622012500095

Koyuncugil, A. S., & Ozgulbas, N. (2012). Financial early warning system model and data mining application for risk detection. Expert Systems with Applications, 396(6), 6238-6253. https://doi.org/10.1016/j.eswa.2011.12.021

Kritzman, M., & Li, Y. Z. (2010). Skulls, financial turbulence, and risk management. Financial Analysts Journal, 66(5), 30-41. https://doi.org/10.2469/faj.v66.n5.3

Kuzubas, T. U., Saltoglu, B., & Sever, C. (2016). Systemic risk and heterogeneous leverage in banking networks. Physica A: Statistical Mechanics and its Applications, 462, 358-375. https://doi.org/10.1016/j.physa.2016.06.085

Ladley, D. (2013). Contagion and risk-sharing on the inter-bank market. Journal of Economic Dynamics and Control, 37(7), 1384-1400. https://doi.org/10.1016/j.jedc.2013.03.009

Laeven, L., Ratnovski, L., & Tong, H. (2016). Bank size, capital, and systemic risk: Some international evidence. Journal of Banking & Finance, 69, 25-34. https://doi.org/10.1016/j.jbankfin.2015.06.022

Leitner, Y. (2005). Financial networks: contagion, commitment, and private sector bailouts. Journal of Finance, 60(6), 2925-2953. https://doi.org/10.1111/j.1540-6261.2005.00821.x

Li, H., Liu, H., Siganos, A., & Zhou, M. M. (2016). Bank regulation, financial crisis, and the announcement effects of seasoned equity offerings of US commercial banks. Journal of Financial Stability, 25, 37-46. https://doi.org/10.1016/j.jfs.2016.06.007

Li, J. P., Feng, J. C., Sun, X. L., & Li, M. L. (2012). Risk integration mechanisms and approaches in banking industry. International Journal of Information Technology & Decision Making, 11(06), 1183-1213. https://doi.org/10.1142/S0219622012500320

Li, S., Wang, M., & He, J. (2013). Prediction of banking systemic risk based on support vector machine. Mathematical Problems in Engineering, 2013(1), 1-5. https://doi.org/10.1155/MPE.2005.1

Li, N., Liang, X., Li, X. L., Wang, C., & Wu, D. D. S. (2009). Network environment and financial risk using machine learning and sentiment analysis. Human and Ecological Risk Assessment: An International Journal, 15(2), 227-252. https://doi.org/10.1080/10807030902761056

Li, Y., Spigt, R., & Swinkels, L. (2017). The impact of FinTech start-ups on incumbent retail banks’ share prices. Financial Innovation, 3(1), 26. https://doi.org/10.1186/s40854-017-0076-7

Liang, N. (2013). Systemic risk monitoring and financial stability. Journal of Money, Credit and Banking, 45, 129-135. https://doi.org/10.1111/jmcb.12039

Liang, Y. (2016). Shadow banking in China: implications for financial stability and macroeconomic rebalancing. The Chinese Economy, 49(3), 148-160. https://doi.org/10.1080/10971475.2016.1159903

Loughran, T., & McDonald, B. (2011). When is a liability not aliability? Textual analysis,dictionaries, and 10-ks. The Journal of Finance, 66(1), 35-65. https://doi.org/10.1111/j.1540-6261.2010.01625.x

Lupu, I. (2015). The indirect relation between corporate governance and financial stability. Procedia Economics and Finance, 22(1), 538-543. https://doi.org/10.1016/S2212-5671(15)00254-3

May, R. M., Levin, S. A., & Sugihara, G. (2008). Complex systems: Ecology for bankers. Nature, 451(7181), 893-895. https://doi.org/10.1038/451893a

McAfee, A., & Brynjolfsson, E. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.

Merton, R. C. (1974). On the pricing of corporate debt: the risk structure of interest rates. The Journal of Finance, 29(2), 449-470.

Meyer, B., Bikdash, M., & Dai, X. (2017). Fine-grained financial news sentiment analysis. SoutheastCon 2017 (pp. 1-8). IEEE. Chalotte, NC, USA. https://doi.org/10.1109/SECON.2017.7925378

Milne, A. (2014). Distance to default and the financial crisis. Journal of Financial Stability, 12(1), 26-36. https://doi.org/10.1016/j.jfs.2013.05.005

Mishkin, F. S. (2007). Chapter 2: An overview of the financial system. Pearson Addison Wesley.

Mitchell, T. (1997). Machine learning (p. 2). McGraw Hill.

Mustafa, F., Khursheed, A., & Fatima, M. (2018). Impact of global financial crunch on financially innovative microfinance institutions in South Asia. Financial Innovation, 4(1), 13. https://doi.org/10.1186/s40854-018-0099-8

Nucera, F., Schwaab, B., Koopman, S. J., & Lucas, A. (2016). The information in systemic risk rankings. Journal of Empirical Finance, 38, 461-475. https://doi.org/10.1016/j.jempfin.2016.01.002

Nyman, R., Gregory, D., Kapadia, S., Smith, R., & Tuckett, D. (2014). News and narratives in financial systems: exploiting big data for systemic risk assessment. Paper presented at ECB Workshop on Big Data for Forecasting and Statistics. London, UK.

O’Halloran, S., Maskey, S., Mcallister, G., Park, D. K., & Chen, K. (2015 August). Big Data and the regulation of financial markets. In 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (pp. 1118-1124). Paris, France. https://doi.org/10.1145/2808797.2808841

Poledna, S., Molina-Borboa, J. L., Martínez-Jaramillo, S., Leij, M. V. D., & Thurner, S. (2015). The multilayer network nature of systemic risk and its implications for the costs of financial crises. Journal of Financial Stability, 20, 70-81. https://doi.org/10.1016/j.jfs.2015.08.001

Posner, E., & Weyl, E. G. (2013). Benefit cost analysis for financial regulation. The American Economic Review, 103(3), 393-397. https://doi.org/10.1257/aer.103.3.393

Prasanna, G., Haldane, A., & Kapadia S. (2011). Complexity, concentration and contagion. Journal of Monetary Economics, 58(5), 453-470. https://doi.org/10.1016/j.jmoneco.2011.05.005

Roukny, T., Bersini, H., Pirotte, H., Caldarelli, G., & Battiston, S. (2013). Default cascades in complex networks: topology and systemic risk. Scientific Reports, 3(1), 2759. https://doi.org/10.1038/srep02759

Price, M., Doran, J. S., Peterson, D. R., & Bliss B. A. (2012). Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 36(4), 992-1011. https://doi.org/10.1016/j.jbankfin.2011.10.013

Sarlin, P. (2016a). Computational tools for systemic risk identification and assessment. Intelligent Systems in Accounting, Finance and Management, 23(1-2). https://doi.org/10.1002/isaf.1389

Sarlin, P. (2016b). Macroprudential oversight, risk communication and visualization. Journal of Financial Stability, 27, 160-179. https://doi.org/10.1016/j.jfs.2015.12.005

Shen, C. W. (2017). A Bayesian networks approach to modeling financial risks of e-logistics investments. International Journal of Information Technology & Decision making, 08(04), 711-726. https://doi.org/10.1142/S0219622009003594

Segoviano, B. M., & Goodhart, C. (2009). Banking stability measures. IMF Working Papers, 23(2), 202-209.

Silva, W., Kimura, H., Sobreiro, V. A. (2017). An analysis of the literature on systemic financial risk: A survey. Journal of Financial Stability, 28, 91-114. https://doi.org/10.1016/j.jfs.2016.12.004

Smailović, J., Grčar, M., Lavrač, N., & Žnidaršič, M. (2014). Stream-based active learning for sentiment analysis in the financial domain. Information Sciences, 285(1), 181-203. https://doi.org/10.1016/j.ins.2014.04.034

Souza, S. R. S. de. (2016). Capital requirements, liquidity and financial stability: the case of Brazil. Journal of Financial Stability, 25(1), 179-192. https://doi.org/10.1016/j.jfs.2015.10.001

Souza, S. R. S. de, Silva, T. C., Tabak, B. M., & Guerra, S. M. (2016). Evaluating systemic risk using bank default probabilities in financial networks. Journal of Economic Dynamics and Control, 66(1), 54-75. https://doi.org/10.1016/j.jedc.2016.03.003

Stein, J. (2011). The crisis, fed, quants and stochastic optimal control. Economic Modelling, 28(1-2), 272-280. https://doi.org/10.1016/j.econmod.2010.09.002

Tromp, E., Pechenizkiy, M., & Gaber, M. M. (2017). Expressive modeling for trusted big data analytics: techniques and applications in sentiment analysis. Big Data Analytics, 2017(2), 5. https://doi.org/10.1186/s41044-016-0018-9

Tsai, M. F., & Wang, C. J. (2013, March). Risk ranking from financial reports. Advances in Information Retrieval. 35th European Conference on IR Research, ECIR (pp. 804-807). Moscow, Russia. https://doi.org/10.1007/978-3-642-36973-5_89

Tsai, M. F., & Wang, C. J. (2017). On the risk prediction and analysis of soft information in finance reports. European Journal of Operational Research, 257(1), 243-250. https://doi.org/10.1016/j.ejor.2016.06.069

Tsenova, T. (2014). International monetary transmission with bank heterogeneity and default risk. Annals of Finance, 10(2), 217-241. https://doi.org/10.1007/s10436-013-0241-6

Vallascas, F., & Keasey, K. (2012). Bank resilience to systemic shocks and the stability of banking systems: small is beautiful. Journal of International Money and Finance, 31(6), 1745-1776. https://doi.org/10.1016/j.jimonfin.2012.03.011

Walter, I. (2012). Universal banking and financial architecture. The Quaterly Review of Economics and Finance, 52(2), 114-122. https://doi.org/10.1016/j.qref.2011.12.007

Wu, D. D., & Olson, D. (2010). Enterprise risk management: Coping with model risk in a large bank. Journal of the Operational Research Society, 61(2), 179-190. https://doi.org/10.1057/jors.2008.144

Xiong, X., Wen, M., Zhang, W., & Zhang, Y. J. (2011). Cross-market financial risk analysis: an agent-based computational finance. International Journal of Information Technology & Decision Making, 10(03), 563-584. https://doi.org/10.1142/S0219622011004464

Xu, L. D., He, W., & Li, S. (2014). Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233-2243. https://doi.org/10.1109/TII.2014.2300753

Wu, W., & Kou, G. (2016). A group consensus model for evaluating real estate investment alternatives. Financial Innovation, 2(1), 8. https://doi.org/10.1186/s40854-016-0027-8

Wymeersch, E. (2010). The Reforms of the European Financial Supervisory System – An overview. European Company and Financial Law Review, 7(2), 240-265. https://doi.org/10.1515/ecfr.2010.240

Yang, S., Guo, K., Li, J., Zhong, Y., Liu, R., & Feng, Z. (2014). Framework formation of financial data classification standard in the era of the big data. Procedia Computer Science, 30, 88-96. https://doi.org/10.1016/j.procs.2014.05.385

Zhang, L., Hu, H., & Zhang, D. (2015). A credit risk assessment model based on SVM for small and medium enterprises in supply chain finance. Financial Innovation, 1, 14. https://doi.org/10.1186/s40854-015-0014-5