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Network resilience in the financial sectors: advances, key elements, applications, and challenges for financial stability regulation

    Gang Kou Affiliation
    ; Xiangrui Chao Affiliation
    ; Yi Peng Affiliation
    ; Fan Wang Affiliation

Abstract

Security against systemic financial risks is the main theme for financial stability regulation. As modern financial markets are highly interconnected and complex networks, their network resilience is an important indicator of the ability of the financial system to prevent risks. To provide a comprehensive perspective on the network resilience of financial networks, we review the main advances in the literature on network resilience and financial networks. Further, we review the key elements and applications of financial network resilience processing in financial regulation, including financial network information, network resilience measures, financial regulatory technologies, and regulatory applications. Finally, we discuss ongoing challenges and future research directions from the perspective of resilience-based financial systemic risk regulation.

Keyword : financial network, network resilience, financial stability regulation, systemic risk

How to Cite
Kou, G., Chao, X., Peng, Y., & Wang, F. (2022). Network resilience in the financial sectors: advances, key elements, applications, and challenges for financial stability regulation. Technological and Economic Development of Economy, 28(2), 531–558. https://doi.org/10.3846/tede.2022.16500
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Mar 28, 2022
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References

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

Almoghathawi, Y., & Barker, K. (2019). Component importance measures for interdependent infrastructure network resilience. Computers & Industrial Engineering, 133, 153–164. https://doi.org/10.1016/j.cie.2019.05.001

Amini, H., Cont, R., & Minca, A. (2016). Resilience to contagion in financial networks. Mathematical Finance, 26(2), 329–365. https://doi.org/10.1111/mafi.12051

Anagnostopoulos, I. (2018) Fintech and regtech: Impact on regulators and banks. Journal of Economics and Business, 100, 7–25. https://doi.org/10.1016/j.jeconbus.2018.07.003

Anand, K., Lelyveld, I. V., Banai, A., Friedrich, S., Garratt, R., & Haaj, G., Fique, J., Hansen, I., Jaramillo, S. M., Lee, H., Molina-Borboa, J. L., Nobili, S., Rajan, S., Salakhova, D., Silva, Th. Ch., Silvestri, L., & Stancato de Souza, S. R. (2018). The missing links: A global study on un-covering financial network structures from partial data. Journal of Financial Stability, 35, 107–119. https://doi.org/10.1016/j.jfs.2017.05.012

Arner, D. W., Zetzsche, D. A., Buckley, R. P., & Barberis, J. N. (2017). FinTech and RegTech: Enabling innovation while preserving financial stability. Georgetown Journal of International Affairs, 18(3), 47–58. https://doi.org/10.1353/gia.2017.0036

Au, A. (2021). FinTech innovation and knowledge flows in Hong Kong’s financial sector: A social network analysis approach. Journal of Asia Business Studies. https://doi.org/10.1108/JABS-09-2020-0381

Avdjiev, S., & Takáts, E. (2019). Monetary policy spillovers and currency networks in cross-border bank lending: Lessons from the 2013 Fed taper tantrum. Review of Finance, 23(5), 993–1029. https://doi.org/10.1093/rof/rfy030

Barbera, C., Jones, M., Korac, S., Saliterer, I., & Steccolini, I. (2017). Governmental financial resilience under austerity in Austria, England and Italy: How do local governments cope with financial shocks? Public Administration, 95(3), 670–697. https://doi.org/10.1111/padm.12350

Bargigli, L., Di Iasio, G., Infante, L., Lillo, F., & Pierobon, F. (2014). The multiplex structure of interbank networks. Quantitative Finance, 15(4), 673–691. https://doi.org/10.1080/14697688.2014.968356

Barzel, B., & Barabási, A. L. (2013). Universality in network dynamics. Nature Physics, 9(10), 673–681. https://doi.org/10.1038/nphys2741

Barzel, B., Liu, Y. Y., & Barabási, A. L. (2015). Constructing minimal models for complex system dynamics. Nature Communications, 6(1), 1–8. https://doi.org/10.1038/ncomms8186

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

Bhattacharya, M., Inekwe, J. N., & Valenzuela, M. R. (2020). Credit risk and financial integration: An application of network analysis. International Review of Financial Analysis, 72, 101588. https://doi.org/10.1016/j.irfa.2020.101588

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

Brancaccio, E., Giammetti, R., Lopreite, M., & Puliga, M. (2018). Centralization of capital and financial crisis: A global network analysis of corporate control. Structural Change and Economic Dynamics, 45, 94–104. https://doi.org/10.1016/j.strueco.2018.03.001

Bruneau, M., Chang, S. E., Eguchi, R. T., Lee, G. C., O’Rourke, Th. D., Reinhorn, A. M., Shinozuka, M., Tierney, K., Wallace, W. A., & von Winterfeldt, D. (2003). A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra, 19(4), 733–752. https://doi.org/10.1193/1.1623497

Buckley, R. P., Arner, D. W., Zetzsche, D. A., & Weber, R. H. (2020). The road to Reg-Tech: the (astonishing) example of the European Union. Journal of Banking Regulation, 21(1), 26–36. https://doi.org/10.1057/s41261-019-00104-1

Capponi, A., Corell, F. C., & Stiglitz, J. E. (2020). Optimal bailouts and the doom loop with a financial network (NBER Working Paper No. 27074). https://doi.org/10.3386/w27074

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

Chabot, M., Bertrand, J. L., & Thorez, E. (2019). Resilience of United Kingdom financial institutions to major uncertainty: A network analysis related to the Credit Default Swaps market. Journal of Business Research, 101, 70–82. https://doi.org/10.1016/j.jbusres.2019.04.003

Chao, X., Dong, Y., Kou, G., & Peng, Y. (2021a). How to determine the consensus threshold in group decision making: a method based on efficiency benchmark using benefit and cost insight. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03927-8

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

Chao, X., Kou, G., Peng, Y., & Alsaadi, F. E. (2019). Behavior monitoring methods for trade-based money laundering integrating macro and micro prudential regulation: A case from china. Technological and Economic Development of Economy, 25(6), 1081–1096. https://doi.org/10.3846/tede.2019.9383

Chao, X., Kou, G., Peng, Y., & Herrera-Viedma, E. (2021b). Large-scale group decision-making with noncooperative behaviors and heterogeneous preferences: An application in financial inclusion. European Journal of Operational Research, 288(1), 271–293. https://doi.org/10.1016/j.ejor.2020.05.047

Chao, X., Kou, G., Peng, Y., Herrera-Viedma, E., & Herrera, F. (2021c). An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement. Information Sciences, 575, 499–527. https://doi.org/10.1016/j.ins.2021.06.047

Chen, X., Qiu, J., Reedman, L., & Dong, Z. Y. (2019). A statistical risk assessment framework for distribution network resilience. IEEE Transactions on Power Systems, 34(6), 4773–4783. https://doi.org/10.1109/TPWRS.2019.2923454

Chiou, S. W. (2018). A traffic-responsive signal control to enhance road network resilience with hazmat transportation in multiple periods. Reliability Engineering & System Safety, 175, 105–118. https://doi.org/10.1016/j.ress.2018.03.016

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

Chowdhury, B., Dungey, M., Kangogo, M., Sayeed, M. A., & Volkov, V. (2019). The changing network of financial market linkages: The Asian experience. International Review of Financial Analysis, 64, 71–92. https://doi.org/10.1016/j.irfa.2019.05.003

Christopher, N., & Tesar, L. L. (2019). The value of systemic unimportance: The case of MetLife. Review of Finance, 23(6), 1069–1078. https://doi.org/10.1093/rof/rfy037

Chu, Y., Deng, S., & Xia, C. (2020). Bank geographic diversification and systemic risk. The Review of Financial Studies, 33(10), 4811–4838. https://doi.org/10.1093/rfs/hhz148

Colladon, A. F., & Remondi, E. (2017). Using social network analysis to prevent money laundering. Expert Systems with Applications, 67, 49–58. https://doi.org/10.1016/j.eswa.2016.09.029

Correa, R., Garud, K., Londono, J. M., & Mislang, N. (2021). Sentiment in central banks’ financial stability reports. Review of Finance, 25(1), 85–120. https://doi.org/10.1093/rof/rfaa014

Csóka, P., & Herings, P. J. J. (2020). An axiomatization of the proportional rule in financial networks. Management Science, 67(5), 2799–2812. https://doi.org/10.1287/mnsc.2020.3700

Csóka, P., & Herings, P. J.-J. (2017). Decentralized clearing in financial networks. Management Science, 64(10), 4681–4699. https://doi.org/10.1287/mnsc.2017.2847

Currie, W. L., Gozman, D. P., & Seddon, J. J. (2018). Dialectic tensions in the financial markets: a longitudinal study of pre-and post-crisis regulatory technology. Journal of Information Technology, 33(4), 304–325. https://doi.org/10.1057/s41265-017-0047-5

Dehghanian, P., Aslan, S., & Dehghanian, P. (2018). Maintaining electric system safety through an enhanced network resilience. IEEE Transactions on Industry Applications, 54(5), 4927–4937. https://doi.org/10.1109/TIA.2018.2828389

Dev, P. (2018). Group identity in a network formation game with cost sharing. Journal of Public Economic Theory, 20(3), 390–415. https://doi.org/10.1111/jpet.12286

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

Esmalifalak, H. (2021). Euclidean (dis)similarity in financial network analysis. Global Finance Journal, 100616. https://doi.org/10.1016/j.gfj.2021.100616

Fabio, C., & Mario, E. (2018). Liquidity flows in interbank networks. Review of Finance, 22(4), 1291–1334. https://doi.org/10.1093/rof/rfy013

Financial Stability Board. (2019). Cyber lexicon [EB/OL]. https://www.fsb.org/2018/11/cyber-lexicon/

Floyd, E., Li, N., & Skinner, D. J. (2015). Payout policy through the financial crisis: The growth of repurchases and the resilience of dividends. Journal of Financial Economics, 118(2), 99–316. https://doi.org/10.1016/j.jfineco.2015.08.002

Gao, J. (2021). Managing liquidity in production networks: The role of central firms. Review of Finance, 25(3), 819–861. https://doi.org/10.1093/rof/rfaa025

Gao, J., Barzel, B., & Barabási, A. L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307–312. https://doi.org/10.1038/nature16948

Gao, J., Liu, X., Li, D., & Havlin, S. (2015). Recent progress on the resilience of complex networks. Energies, 8(10), 12187–12210. https://doi.org/10.3390/en81012187

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

Ghorbani-Renani, N., Gonzlez, A. D., Barker, K., & Morshedlou, N. (2020). Protection-interdiction-restoration: Tri-level optimization for enhancing interdependent network resilience. Reliability Engineering and System Safety, 199, 106907. https://doi.org/10.1016/j.ress.2020.106907

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., Sarlin, P., & Spelta, A. (2020). The interconnected nature of financial systems: Direct and common exposures. Journal of Banking & Finance, 112, 105149. https://doi.org/10.1016/j.jbankfin.2017.05.010

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

Hautsch, N., Schaumburg, J., & Schienle, M. (2015). Financial network systemic risk contributions. Review of Finance, 19(2), 685–738. https://doi.org/10.1093/rof/rfu010

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

Holling, C. S. (1973). Resilience and stability of ecological systems. Annual Review of Ecology and Systematics, 4, 1–23. https://doi.org/10.1146/annurev.es.04.110173.000245

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

Hu, K. H., Hsu, M. F., Chen, F. H., & Liu, M. Z. (2021). Identifying the key factors of subsidiary supervision and management using an innova-tive hybrid architecture in a big data environment. Financial Innovation, 7, 10.

Huang, W. Q., & Wang, D. (2020). Financial network linkages to predict economic output. Finance Research Letters, 33, 101206. https://doi.org/10.1016/j.frl.2019.06.004

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, 183–196. https://doi.org/10.1016/j.physa.2016.03.034

Ilollari, O. F., & Gjino, G. (2013). Financial crisis. Implementation of macro-and micro-prudential regulation. Review of Applied Socio-Economic Research, 5(1), 83–91.

Inekwe, J. N., Jin, Y., & Valenzuela, M. R. (2018). Global financial network and liquidity risk. Australian Journal of Management, 43(4), 593–613. https://doi.org/10.1177/0312896218766219

Isogai, T. (2017). Dynamic correlation network analysis of financial asset returns with network clustering. Applied Network Science, 2(1), 8. https://doi.org/10.1007/s41109-017-0031-6

Jing, F., & Chao, X. (2021). Fairness concern: An equilibrium mechanism for consensus-reaching game in group decision-making. Information Fusion, 72, 147–160. https://doi.org/10.1016/j.inffus.2021.02.024

Jing, F., & Chao, X., (2022). Forecast horizons for a two-echelon dynamic lot-sizing problem. Omega, 110, 102613. https://doi.org/10.1016/j.omega.2022.102613

Jun, J., & Yeo, E. (2021). Central bank digital currency, loan supply, and bank failure risk: A microeconomic approach. Financial Innovation, 7, 81. https://doi.org/10.1186/s40854-021-00296-4

Kaffash, S., & Marra, M. (2017). Data envelopment analysis in financial services: A citations network analysis of banks, insurance companies and money market funds. Annals of Operations Research, 253(1), 307–344. https://doi.org/10.1007/s10479-016-2294-1

Kaiser-Bunbury, C., Mougal, J., Whittington, A., Valentin, T., Gabriel, R., Olesen, J. M., & Blüthgen, N. (2017). Ecosystem restoration strength-ens pollination network resilience and function. Nature, 542, 223–227. https://doi.org/10.1038/nature21071

Khabazian, A., & Peng, J. (2019). Vulnerability analysis of the financial network. Management Science, 65(7), 3302–3321. https://doi.org/10.1287/mnsc.2018.3106

Klapper, L., & Lusardi, A. (2020). Financial literacy and financial resilience: Evidence from around the world. Financial Management, 49(3), 589–614. https://doi.org/10.1111/fima.12283

Korniyenko, M. Y., Patnam, M., del Rio-Chanon, R. M., & Porter, M. A. (2018). Evolution of the global financial network and contagion: A new approach. International Monetary Fund, 2018(113), 1–41. https://doi.org/10.5089/9781484353240.001

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

Kou, G., Ergu, D., & Shang, J. (2014). Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction. European Journal of Operational Research, 236(1), 261–271. https://doi.org/10.1016/j.ejor.2013.11.035

Kou, G., Ergu, D., Lin, C., & Chen, Y. (2016). Pairwise comparison matrix in multiple criteria decision making. Technological and Economic Development of Economy, 22(5), 738–765. https://doi.org/10.3846/20294913.2016.1210694

Kou, G., Peng, Y., Chao, X., Herrera-Viedma, E., & Alsaadi, F. E. (2021b). A geometrical method for consensus building in GDM with incomplete heterogeneous preference information. Applied Soft Computing, 105, 107224. https://doi.org/10.1016/j.asoc.2021.107224

Kou, G., Xu, Y., Peng, Y., Shen, F., Chen, Y., Chang, K., & Kou, S. (2021a). Bankruptcy prediction for SMEs using transactional data and two-stage multiobjective feature selection. Decision Support Systems, 140, 113429. https://doi.org/10.1016/j.dss.2020.113429

Kwon, J. H. (2021). On the factors of Bitcoin’s value at risk. Financial Innovation, 7, 87. https://doi.org/10.1186/s40854-021-00297-3

Lee, J. W., & Nobi, A. (2018). State and network structures of stock markets around the global financial crisis. Computational Economics, 51(2), 195–210. https://doi.org/10.1007/s10614-017-9672-x

Li, S., Yuan, L., & Wenjun, L. (2019). China’s regional financial risk spatial correlation network and regional contagion effect: 2009–2016. Management Review, 31(8), 35–48.

Li, Y., Jiang, S., Wei, Y. & Wang, S. (2021). Take Bitcoin into your portfolio: A novel ensemble portfolio optimization framework for broad commodity assets. Financial Innovation, 7, 63. https://doi.org/10.1186/s40854-021-00281-x

Lin, C., Kou, G., Peng, Y., & Alsaadi, F. E. (2020). Aggregation of the nearest consistency matrices with the acceptable consensus in AHP-GDM. Annals of Operations Research. https://doi.org/10.1007/s10479-020-03572-1

Liu, J., Wu, C., & Li, Y. (2019). Improving financial distress prediction using financial network-based information and GA-based gradient boosting method. Computational Economics, 53(2), 851–872. https://doi.org/10.1007/s10614-017-9768-3

Liu, S., Caporin, M., & Paterlini, S. (2021). Dynamic network analysis of North American financial institutions. Finance Research Letters, 42, 101921. https://doi.org/10.1016/j.frl.2021.101921

Lui, A. (2010). Macro and micro prudential regulatory failures amongst financial institutions in the United Kingdom: Lessons from Australia. Journal of Financial Regulation and Compliance. https://doi.org/10.2139/ssrn.1716264

Lusardi, A., Hasler, A., & Yakoboski, P. J. (2021). Building up financial literacy and financial resilience. Mind & Society, 20, 181–187. https://doi.org/10.1007/s11299-020-00246-0

Magner, N. S., Lavin, J. F., Valle, M. A., & Hardy, N. (2020). The volatility forecasting power of financial network analysis. Complexity, 2020, 7051402. https://doi.org/10.1155/2020/7051402

Markose, S., Giansante, S., & Shaghaghi, A. R. (2012). Too interconnected to fail’ financial network of US CDS market: Topological fragility and systemic risk. Journal of Economic Behavior & Organization, 83(3), 627–646. https://doi.org/10.1016/j.jebo.2012.05.016

May, R. M., Levin, S. A., & Sugihara, G. (2008). Complex systems: Ecology for bankers. Nature, 451(7181), 893–894. https://doi.org/10.1038/451893a

McCallig, J., Robb, A., & Rohde, F. (2019). Establishing the representational faithfulness of financial accounting information using multiparty security, network analysis and a blockchain. International Journal of Accounting Information Systems, 33, 47–58. https://doi.org/10.1016/j.accinf.2019.03.004

Mensi, W., Rehman, M. U., Shafullah, M., Shafullah, M., Al Yahyaee, K. H., & Sensoy, A. (2021). Correction to: High frequency multiscale relationships among major cryptocurrencies: portfolio management implications. Financial Innovation, 7, 82. https://doi.org/10.1186/s40854-021-00298-2

Moulin, H., & Sethuraman, J. (2013). The bipartite rationing problem. Operations Research, 61(5), 1087–1100. https://doi.org/10.1287/opre.2013.1199

Nie, C. X., & Song, F. T. (2018). Constructing financial network based on PMFG and threshold method. Physica A: Statistical Mechanics and its Applications, 495, 104–113. https://doi.org/10.1016/j.physa.2017.12.037

Packin, N. G. (2018). RegTech, compliance and technology judgment rule. Chicago-Kent Law Review, 93, 193–218.

Persaud, A. (2009). Macro-prudential regulation. Crisis response note. No. 6. World Bank, Washington, DC. https://openknowledge.worldbank.org/handle/10986/10243

Pimm, S. L. (1984). The complexity and stability of ecosystems. Nature, 307, 321–326. https://doi.org/10.1038/307321a0

Poledna, S., Molina-Borboa, J. L., Martínez-Jaramillo, S., Leij, M. V. D., & Thrner, 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

Ruan, L., Li, C., Zhang, Y., & Wang, H. (2019). Soft computing model based financial aware spatiotemporal social network analysis and visualiza-tion for smart cities. Computers, Environment and Urban Systems, 77, 101268. https://doi.org/10.1016/j.compenvurbsys.2018.07.002

Salignac, F., Marjolin, A., Reeve, R., & Muir, K. (2019). Conceptualizing and measuring financial resilience: A multidimensional framework. Social Indicators Research, 145(1), 17–38. https://doi.org/10.1007/s11205-019-02100-4

Shi, N., Zhou, S., Wang, F., Xu, S., & Xiong, S. (2014). Horizontal cooperation and information sharing between suppliers in the manufacturer–supplier triad. International Journal of Production Research, 52(15), 4526–4547. https://doi.org/10.1080/00207543.2013.869630

Simmie, J., & Martin, R. (2010). The economic resilience of regions: Towards an evolutionary approach. Cambridge Journal of the Regions, Economy and Society, 3(1), 27–43. https://doi.org/10.1093/cjres/rsp029

Singh, C., Lin, W., & Ye, Z. (2021). Can artificial intelligence, RegTech and CharityTech provide Effective Solutions for anti-money laundering and counter-terror financing initiatives in charitable fundraising. Journal of Money Laundering Control, 24(3), 464–482. https://doi.org/10.1108/JMLC-09-2020-0100

Tang, Y., Xiong, J. J., Jia, Z. Y., & Zhang, Y. C. (2018). Complexities in financial network topological dynamics: Modeling of emerging and de-veloped stock markets. Complexity, 4680140. https://doi.org/10.1155/2018/4680140

The White House. (2013, February 12). Presidential Policy Directive – Critical infrastructure security and resilience [EB/OL]. https://obamawhitehouse.archives.gov/the-press-office/2013/02/12/presidential-policy-directive-critical-infrastructure-security-and-resil

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

Tu, C., Grilli, J., Schuessler, F., & Suweis, S. (2017). Collapse of resilience patterns in generalized Lotka-Volterra dynamics and beyond. Physical Review E, 95(6), 062307. https://doi.org/10.1103/PhysRevE.95.062307

Wall, L. D. (2018). Some financial regulatory implications of artificial intelligence. Journal of Economics and Business, 100, 55–63. https://doi.org/10.1016/j.jeconbus.2018.05.003

Wang, F., Lai, X., & Shi, N. (2011). A multi-objective optimization for green supply chain network design. Decision Support Systems, 51(2), 262–269. https://doi.org/10.1016/j.dss.2010.11.020

Wetzel, P., & Hofmann, E. (2019). Supply chain finance, financial constraints and corporate performance: An explorative network analysis and future research agenda. International Journal of Production Economics, 216, 364–383. https://doi.org/10.1016/j.ijpe.2019.07.001

William, N A. (2000) Social and ecological resilience: Are they related? Progress in Human Geography, 24(3), 347–364. https://doi.org/10.1191/030913200701540465

Xiao, F., & Ke, J. (2021). Pricing, management and decision-making of financial markets with artificial intelligence: introduction to the issue. Financial Innovation, 7, 85. https://doi.org/10.1186/s40854-021-00302-9

Yang, J., Yu, Z., & Ma, J. (2019). China’s financial network with international spillovers: A first look. Pacific-Basin Finance Journal, 58, 101222. https://doi.org/10.1016/j.pacfin.2019.101222

Yazıcıoğlu, A. Y., Roozbehani, M., & Dahleh, M. A. (2016, December). Resilience of locally routed network flows: More capacity is not always better. In 2016 IEEE 55th Conference on Decision and Control (CDC) (pp. 111–116). Las Vegas, NV, USA. IEEE. https://doi.org/10.1109/CDC.2016.7798255

Zha, Q., Kou, G., Zhang, H., Liang, H., Chen, X., Li, C.-C., & Dong, Y. (2020). Opinion dynamics in finance and business: A literature review and research opportunities. Financial Innovation, 6, 44.

Zhang, J., Kou, G., Peng, Y., & Zhang, Y. (2021). Estimating priorities from relative deviations in pairwise comparison matrices. Information Sciences, 552, 310–327. https://doi.org/10.1016/j.ins.2020.12.008

Zhou, C. (2010). Why the micro-prudential regulation fails? The impact on systemic risk by imposing a capital requirement (De Nederlandsche Bank Working Paper No. 256). https://doi.org/10.2139/ssrn.1949052