Volume 11 • Issue 1 • PP: 54-61 • 2024
Predictive Analytics for Financial Risk Management in Dynamic Markets
View PDF Abstract
effective risk management is an indispensable requirement for improving the flow of transactions in dynamic financial markets. To this end, this study presents an applied predictive analytics methodology, that integrate gradient boosting algorithm to model the risk behavior in dynamic markets. This study, based on predictive analytics in monetary and financial systems, faces an urgent need for robust models that can overcome the uncertainties inherent in dynamic markets. Holistic experimentations on public case study of U.S retail data demonstrate the predictive power of the proposed approach of the state-of-the-art techniques across different performance metrics. This in turn highlights the nuanced interaction between variables and delivering intuitions into crucial risk determining factor.
Keywords
References
[1] Broby, D. (2022). The use of predictive analytics in finance. The Journal of Finance and Data Science, 8, 145-161.
[2] Gu, X., Mamon, R., Duprey, T., & Xiong, H. (2021). Online estimation for a predictive analytics platform with a financial-stability-analysis application. European Journal of Control, 57, 205-221.
[3] Yang, R., Yu, L., Zhao, Y., Yu, H., Xu, G., Wu, Y., & Liu, Z. (2020). Big data analytics for financial Market volatility forecast based on support vector machine. International Journal of Information Management, 50, 452-462.
[4] Lin, E. M., Sun, E. W., & Yu, M. T. (2020). Behavioral data-driven analysis with Bayesian method for risk management of financial services. International Journal of Production Economics, 228, 107737.
[5] Feng, R., & Qu, X. (2022). Analyzing the Internet financial market risk management using data mining and deep learning methods. Journal of Enterprise Information Management, 35(4/5), 1129-1147.
[6] Dash Wu, D. (2020). Data intelligence and risk analytics. Industrial Management & Data Systems, 120(2), 249-252.
[7] Ghosh, I., Jana, R. K., & Sanyal, M. K. (2019). Analysis of temporal pattern, causal interaction and predictive modeling of financial markets using nonlinear dynamics, econometric models and machine learning algorithms. Applied Soft Computing, 82, 105553.
[8] Han, L. (2019). Correlation predictive modeling of financial markets. Procedia Computer Science, 154, 738-743.
[9] Qiao, Q., & Beling, P. A. (2016). Decision analytics and machine learning in economic and financial systems. Environment Systems and Decisions, 36, 109-113.
[10] Bouchaud, J. P., & Potters, M. (2003). Theory of financial risk and derivative pricing: from statistical physics to risk management. Cambridge university press.
[11] Kim, A., Yang, Y., Lessmann, S., Ma, T., Sung, M. C., & Johnson, J. E. (2020). Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. European Journal of Operational Research, 283(1), 217-234.
[12] Bahrami, M., & Shokouhyar, S. (2022). The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view. Information Technology & People, 35(5), 1621-1651.
[13] Phan, D. T., & Trang Tran, L. Q. (2022). Building a Conceptual Framework for Using Big Data Analytics in the Banking Sector.
[14] Zhu, X., & Yang, Y. (2021). Big data analytics for improving financial performance and sustainability. Journal of Systems Science and Information, 9(2), 175-191.
[15] Rasmussen, J. (1997). Risk management in a dynamic society: a modelling problem. Safety science, 27(2-3), 183-213.
[16] Clintworth, M., Lyridis, D., & Boulougouris, E. (2021). Financial risk assessment in shipping: a holistic machine learning based methodology. Maritime Economics & Logistics, 1-32.
[17] Dicuonzo, G., Galeone, G., Zappimbulso, E., & Dell'Atti, V. (2019). Risk management 4.0: The role of big data analytics in the bank sector. International Journal of Economics and Financial Issues, 9(6), 40-47.
[18] Shih, D. H., Hsu, H. L., & Shih, P. Y. (2019, April). A study of early warning system in volume burst risk assessment of stock with Big Data platform. In 2019 IEEE 4th international conference on cloud computing and big data analysis (ICCCBDA) (pp. 244-248). IEEE.
[19] Haile, I. M. (2020). Data Analytics in Financial Institutions: How Text Analytics Can Help in Risk Management (Doctoral dissertation, Colorado Technical University).
[20] Kothandapani, H. P. (2023). Applications of Robotic Process Automation in Quantitative Risk Assessment in Financial Institutions. International Journal of Business Intelligence and Big Data Analytics, 6(1), 40-52.
Cite This Article
Choose your preferred format