It’s the basic premise of promoting the healthy development of rural finance and strengthening macro-prudential supervision to measure the systemic risk of rural finance accurately. We establish the dynamic factor CAPM and make an all-round and multi-angle quantitative study on the systemic risk of rural finance in China by constructing Macro–micro index system and using machine learning to reduce the dimension of high-dimensional data. Our results show that the dynamic factor CAPM of using Macro–micro big data can evaluate systemic risk of rural finance more comprehensively and systematically, and machine learning performs well in processing high-dimensional data. In addition, China's rural financial systemic risk is stable compared with the Shanghai and Shenzhen main markets, but it is also susceptible to macro and micro influenced factors. Finally, it is pointed out that the early warning system of rural financial systemic risk could be constructed at macro and micro level, respectively.

References

• Acharya, V. V., Pedersen, L. H., Philippon, T., & Richardson, M. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2–47. https://doi.org/10.1093/rfs/hhw088
• Adrian, T., Crump, R. K., & Moench, E. (2015). Regression-based estimation of dynamic asset pricing models. Journal of Financial Economics, 118(2), 211–244. https://doi.org/10.1016/j.jfineco.2015.07.004
• Ang, A., Liu, J., & Schwarz, K. (2020). Using stocks or portfolios in tests of factor models. Journal of Financial and Quantitative Analysis, 55(3), 709–750. https://doi.org/10.1017/S0022109019000255
• Benoit, S., Colletaz, G., Hurlin, C., & Perignon, C. (2013). A theoretical and empirical comparison of systemic risk measures. SSRN Electronic Journal, https://doi.org/10.2139/ssrn.1973950
• Billio, M., Getmansky, M., & Pelizzon, L. (2012). Dynamic risk exposures in hedge funds. Computational Statistics & Data Analysis, 56(11), 3517–3532. https://doi.org/10.1016/j.csda.2010.08.015 
• Bisias, D., Flood, M. D., Lo, A. W., & Valavanis, S. (2012). A survey of systemic risk analytics. Annual Review of Financial Economics, 76(4), 119–131. https://doi.org/10.2139/ssrn.1983602 
• Boguth, O., Carlson, M., Fisher, A., & Simutin, M. (2016). Horizon effects in average returns: The role of slow information diffusion. The Review of Financial Studies, 29(8), 2241–2281. https://doi.org/10.1093/rfs/hhw024
• Castellani, D., Vigan0, L., & Tamre, B. (2014). A discrete choice analysis of smallholder farmers’ preferences and willingness to pay for weather derivatives: Evidence from Ethiopia. Journal of Applied Business Research (JABR), 30(6), 1671–1692. https://doi.org/10.19030/jabr.v30i6.8882 
• Cederburg, S., & O’Doherty,, M. S. (2016). Does it pay to bet against beta? On the conditional performance of the beta anomaly. The Journal of Finance, 71(2), 737–774. https://doi.org/10.1111/jofi.12383
• Chen, W., & Wang, X. J. (2010). Appraisal on intellectual capital and innovation capability: An empirical study based on the 20 years’ panel data of China. Science of Science and Management of S.& T. in China, 31(5), 193–199. 
• Cosemans, M., Frehen, R., Schotman, P., & Bauer, R. (2016). Estimating security betas using prior information based on firm fundamentals. Review of Financial Studies, 29(4), 1072–1112. https://doi.org/10.1093/rfs/hhv131 
• Danielsson, J., James, K. R., Valenzuela, M., & Zer, I. (2016). Can we prove a bank guilty of creating systemic risk? A minority report. Journal of Money, Credit and Banking, 48(4), 795–812. https://doi.org/10.1111/jmcb.12318 
• Deng, K. B., Guan, Z. H., & Chen, B. (2018). Macroeconomic policies and systemic risk in China’s stock market: An approach based on integrated hybrid betas. Economic Research Journal in China, 53(8), 68–83. 
• Drobetz, W., & Otto, T. (2021). Empirical asset pricing via machine learning: Evidence from the European stock market. Journal of Asset Management, 22(7), 507–538. https://doi.org/10.1057/s41260-021-00237-x 
• Elabed, G., Bellemare, M. F., Carter, M. R., & Guirkinger, C. (2013). Managing basis risk with multiscale index insurance. Agricultural Economics, 44(4–5), 419–431. https://doi.org/10.1111/agec.12025 
• Ferson, W. E., & Siegel, A. F. (2009). Testing portfolio efficiency with conditioning information. The Review of Financial Studies, 22(7), 2735–2758. https://doi.org/10.1093/rfs/hhn112
• Gandy, A., & Veraart, L. A. M. (2017). A Bayesian methodology for systemic risk assessment in financial networks. Management Science, 63(12), 4428–4446. https://doi.org/10.1287/mnsc.2016.2546 
• Garcia-Jorcano, L., & Sanchis-Marco, L. (2021). Systemic-systematic risk in financial system: A dynamic ranking based on expectiles. International Review of Economics & Finance, 75, 330–365. https://doi.org/10.1016/j.iref.2021.04.001
• Gong, C. M., Luo, D., & Zhao, H. N. (2021). Liquidity risk and the beta premium. Journal of Financial Research, 44(4), 789–814. https://doi.org/10.1111/jfir.12263
• Gong, X. L., Liu, X. H., Xiong, X., & Zhang, W. (2019). Financial systemic risk measurement based on causal network connectedness analysis. International Review of Economics & Finance, 64, 290–307. https://doi.org/10.1016/j.iref.2019.07.004 
• Gu, S. H., Kelly, B., & Xiu, D. C. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009 
• Han, X. K., & Ma, D. G. (2021). China’s systematic financial risk assessment and early warning based on AM-BPNN model. Statistics & Decision in China, 37(4), 138–141. https://doi.org/10.13546/j.cnki.tjyjc.2021.04.030 
• Hansen, L. P., & Richard, S. F. (1987). The role of conditioning information in deducing testable restrictions implied by dynamic asset pricing models. Econometrica, 55(3), 587–613. https://doi.org/10.2307/1913601
• He, C. Y., Wen, Z., Huang, K., & Ji, X. Q. (2022). Sudden shock and stock market network structure characteristics: A comparison of past crisis events. Technological Forecasting and Social Change, 180, 121732. https://doi.org/10.1016/j.techfore.2022.121732
• Hill, R. V., Hoddinott, J., & Kumar, N. (2013). Adoption of weather-index insurance: Learning from willingness to pay among a panel of households in rural Ethiopia. Agricultural Economics, 44(4–5), 385–398. https://doi.org/10.1111/agec.12023
• Jiang, F. W., Ma, T., & Zhang, H. W. (2021). High risk low return? Explanation from machine learning based conditional CAPM model. Journal of Management Sciences in China, 24(1), 109–126. https://doi.org/10.19920/j.cnki.jmsc.2021.01.007 
• Jiang, F. W., Tang, G. H., & Zhou, G. F. (2018). Firm characteristics and Chinese stocks. Journal of Management Science and Engineering, 3(4), 259–283. https://doi.org/10.3724/SP.J.1383.304014 
• Lewellen, J., & Nagel, S. (2006). The conditional CAPM does not explain asset-pricing anomalies. Journal of Financial Economics, 82(2), 289–314. https://doi.org/10.1016/j.jfineco.2005.05.012 
• Miranda, M. J., & Gonzalez-Vega, C. (2011). Systemic risk, index insurance, and optimal management of agricultural loan portfolios in developing countries. American Journal of Agricultural Economics, 93(2), 399–406. https://doi.org/10.1093/ajae/aaq109
• Rapach, D. E., Strauss, J. K., & Zhou, G. (2013). International stock return predictability: What is the role of the United States? The Journal of Finance, 68(4), 1633–1662. https://doi.org/10.1111/jofi.12041 
• Saqib, S. e., Ahmad, M. M., Panezai, S., & Ali, U. (2016). Factors influencing farmers’ adoption of agricultural credit as a risk management strategy: The case of Pakistan. International Journal of Disaster Risk Reduction, 17, 67–76. https://doi.org/10.1016/j.ijdrr.2016.03.008 
• Squartini, T., Almog, A., Caldarelli, G., van Lelyveld, I., Garlaschelli, D., & Cimini, G. (2017). Enhanced capital-asset pricing model for the reconstruction of bipartite financial networks. Physical Review E, 96(3), 032315. https://doi.org/10.1103/PhysRevE.96.032315
• Straetmans, S., & Chaudhry, S. M. (2015). Tail risk and systemic risk of US and Eurozone financial institutions in the wake of the global financial crisis. Journal of International Money and Finance, 58, 191–223. https://doi.org/10.1016/j.jimonfin.2015.07.003
• Tan, Z. M., & Xia, Q. (2020). The relationship between systemic financial risk and macroeconomic fluctuation in China: A study of index measurement and dynamic impact. Financial Theory & Practice in China, (3), 8–16.
• Turvey, C. G. (2011). Microfinance, rural finance, and development: Multiple products for multiple challenges: Discussion. American Journal of Agricultural Economics, 93(2), 415–417. https://doi.org/10.1093/ajae/aaq107 
• Wang, J. S., & Ren, Y. H. (2021). Construction, analysis and prejudgment of the financial stability index of China. Journal of Quantitative & Technological Economics in China, 38(2), 24–42. https://doi.org/10.13653/j.cnki.jqte.20210128.001 
• Yu, L. P., & Liu, J. (2018). Are principal component analysis and factor analysis suitable for scientific and technological evaluation? Taking academic journals as an example. Journal of Modern Information in China, 38(6), 73–79+137.

To cite this article: Wanling Zhou, Sulin Pang & Zhiliang He (2023): The study on systemic risk of rural finance based on macro–micro big data and machine learning, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2023.2238975

To link to this article: https://doi.org/10.1080/24754269.2023.2238975