Dynamic prediction of portfolio riskiness in financial markets based on multi-factor quantitative models

[1] Law, K. K. F., Li, W. K., & Yu, P. L. H. (2020). An empirical evaluation of large dynamic covariance models in portfolio value-at-risk estimation. The Journal of Risk Model Validation, 14(2). Search in Google Scholar

[2] Wang, W., Qi, Y., Jia, B., et al. (2021). Dynamic prediction model of spontaneous combustion risk in goaf based on improved CRITIC-G2-TOPSIS method and its application. PloS ONE, 16(10), e0257499. Search in Google Scholar

[3] Giorgio, C., & Kwon, R. H. (2018). Risk parity portfolio optimization under a Markov regime-switching framework. Quantitative Finance, 19, 1-19. Search in Google Scholar

[4] Spiridon, P., Shevchenko, P. V., & Wei, W. (2018). The impact of model risk on dynamic portfolio selection under multi-period mean-standard-deviation criterion. European Journal of Operational Research. https://doi.org/10.1016/j.ejor.2018.05.021 Search in Google Scholar

[5] Teramukai, S., Okuda, Y., Miyazaki, S., et al. (2016). Dynamic prediction model and risk assessment chart for cardiovascular disease based on on-treatment blood pressure and baseline risk factors. Hypertension Research, 39(2). Search in Google Scholar

[6] Zhang, J. (2021). Dynamic index optimal investment strategy based on stochastic differential equations in financial market options. Wireless Communications and Mobile Computing. Search in Google Scholar

[7] Yixin, Z. (2018). Investment bank risk prediction model based on dynamic parameter neural network. In *Proceedings of the 2018 Conference*. Search in Google Scholar

[8] Yue, X., Bai, J., Liu, Q., et al. (2019). sPortfolio: Stratified visual analysis of stock portfolios. IEEE Transactions on Visualization and Computer Graphics, PP(99), 1-1. Search in Google Scholar

[9] Faia, R., Pinto, T., Vale, Z., et al. (2018). Multi-objective portfolio optimization of electricity markets participation. In Proceedings of the 2018 Conference (pp. 1-6). Search in Google Scholar

[10] Gao, L., & Xiao, J. (2021). Big data credit report in credit risk management of consumer finance. Wireless Communications and Mobile Computing. Search in Google Scholar

[11] Kang, Q. (2019). Financial risk assessment model based on big data. *International Journal of Modeling, Simulation & Scientific Computing, 10*(04), 106-113. Search in Google Scholar

[12] Colaneri, K., Herzel, S., & Nicolosi, M. (2019). The value of knowing the market price of risk. arXiv. https://arxiv.org/abs/1905.02158 Search in Google Scholar

[13] Chen, X., Ruan, X., & Zhang, W. (2021). Dynamic portfolio choice and information trading with recursive utility. Economic Modelling. Search in Google Scholar

[14] Gerlach, J. C., Zhao, C. D., & Sornette, D. (2020). Forecasting financial crashes: A dynamic risk management approach. Swiss Finance Institute Research Paper Series. Search in Google Scholar

[15] Noor, J. (2019). Effect of financial risk on performance of transport firms in Mombasa County. Unpublished Master’s thesis. Search in Google Scholar

[16] Yang, F. (2022). Multi-factor investment model based on TabNet. Journal of Physics: Conference Series, 2171(1), 012057. Search in Google Scholar

[17] Peng, S., Guo, S., & Long, Y. (2022). Large dynamic covariance matrix estimation with an application to portfolio allocation: A semiparametric reproducing kernel Hilbert space approach. arXiv. https://arxiv.org/abs/2201.12345 (Note: Since the specific journal is not provided, it’s cited as an arXiv preprint.) Search in Google Scholar

[18] Anagnostou, I., Squartini, T., Kandhai, D., et al. (2021). Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling. Quantitative Finance, 1-18. Search in Google Scholar

[19] Li, X., & Hao, D. (2018). A study on the risk regulation of financial investment market based on quantitative analysis. Unpublished Master’s thesis. Search in Google Scholar

[20] Demos, G., Sornette, D., Dempster, M., et al. (2019). On the predictability of stock market bubbles: Evidence from LPPLS confidence multi-scale indicators. Quantitative Finance, 19. Search in Google Scholar

[21] Wenhui, L. (2019). Research on asset allocation of insurance companies based on mean-variance model. In Proceedings of the 2019 Conference. Search in Google Scholar

[22] Kiamehr, A. K., Azar, A., & MD Nayeri. (2018). A multi-objective optimization model for designing business portfolio in the oil industry. Engineering, Technology and Applied Science Research, 8(6), 3657-3667. Search in Google Scholar

[23] Zheng, L., Pan, T., Liu, J., et al. (2019). Quantitative trading system based on machine learning in Chinese financial market. Journal of Intelligent and Fuzzy Systems, 38(3), 1-11. Search in Google Scholar

[24] Lehdili, N., & Givi, A. (2018). Efficient computation of Value-at-Risk and expected shortfall in large and heterogeneous credit portfolios: Application to default risk charge. Risk and Decision Analysis, 7(3-4), 91-105. Search in Google Scholar

[25] Leon, C., Kim, G. Y., Martinez, C., et al. (2017). Equity markets’ clustering and the global financial crisis. Quantitative Finance, 17(12). Search in Google Scholar

[26] Burch, T. R., Emery, D. R., & Fuerst, M. E. (2016). Who moves markets in a sudden marketwide crisis? Evidence from 9/11. Journal of Financial and Quantitative Analysis, 51(2). Search in Google Scholar

[27] Sangyup, C., & Chansik, Y. (2022). Uncertainty, financial markets, and monetary policy over the last century. The B.E. Journal of Macroeconomics, 22. Search in Google Scholar

[28] Jason. (2020). Institutional meaning of portfolio. Financial Expo, 7(2). Search in Google Scholar

[29] Lu, J. (2021). Application of risk perspective in investment. Financial Expo. Search in Google Scholar

[30] Hanedar, A. N., Hanedar, E. Y., & Gktan, M. G. (2022). Insider trading on Ottoman sovereign default: The Ottoman general debt bond at European and Istanbul financial markets. Finance Research Letters, 47. Search in Google Scholar

[31] He, Z., Huang, J., Yong, D., et al. (2016). The prediction of passenger flow distribution for urban rail transit based on multi-factor model. In 2016 IEEE International Conference on Intelligent Transportation Engineering (ICITE) (pp. 54-59). IEEE. Search in Google Scholar

[32] Qin, Y., Zhong, Y., Lei, Z., et al. (2020). A hybrid parameter estimation for multi-asset modeling and dynamic allocation based on financial market microstructure model. International Journal on Artificial Intelligence Tools. Search in Google Scholar

[33] Bianchi, S. W., Goldberg, L. R., & Rosenberg, A. (2017). The impact of estimation error on latent factor model forecasts of portfolio risk. Journal of Portfolio Management, 43(May Special Issue). Search in Google Scholar

[34] Zakamulin, V. (2016). Optimal dynamic portfolio risk management. Journal of Portfolio Management, 43(1). Search in Google Scholar

[35] Liu, C., & Arunkumar, N. (2018). Risk prediction and evaluation of transnational transmission of financial crisis based on complex network. Cluster Computing. Search in Google Scholar

[36] Pop, I. D. (2022). COVID-19 crisis, voters’ drivers, and financial markets consequences on US presidential election and global economy. Finance Research Letters, 44. Search in Google Scholar

[37] Shimizu, H., & Shiohama, T. (2019). Multifactor portfolio construction by factor risk parity strategies: An empirical comparison of global stock markets. Asia-Pacific Financial Markets. Search in Google Scholar