Data-Driven Approaches to Financial Market Risk Assessment Using Predictive Analytics
DOI:
https://doi.org/10.62480/tjms.2026.vol54.6852.pp5-16Keywords:
predictive analytics, financial risk management, volatility forecastingAbstract
Financial institutions rely on risk forecasting models to anticipate market volatility and manage exposure to financial shocks. Traditional econometric approaches such as ARIMA and GARCH models have long served as the foundation of financial risk assessment; however, these models often struggle to capture nonlinear relationships and high-dimensional data patterns present in modern financial markets. This study examines whether predictive analytics methods can improve financial market risk assessment compared with traditional econometric models. Using a comparative empirical framework, the analysis evaluates the performance of machine learning algorithms - including Random Forest, Gradient Boosting, and Neural Networks - against benchmark time-series models in forecasting market volatility. The study integrates market indicators, macroeconomic variables, and sentiment signals derived from financial news data. The empirical results indicate that predictive analytics models provide improved forecasting accuracy and stronger performance in detecting financial stress events relative to traditional approaches. These findings highlight the growing role of data-driven methodologies in financial risk management and suggest that integrating alternative data sources can enhance institutional risk monitoring frameworks.
References
1. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.
2. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
4. Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223–236.
5. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
6. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. Review of Financial Studies, 33(5), 2223–2273.
7. Hamilton, J. D. (1994). Time Series Analysis. Princeton University Press.
8. Heaton, J., Polson, N., & Witte, J. (2017). Deep learning in finance. Annual Review of Financial Economics, 9, 145–181.
9. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
10. Tetlock, P. (2007). Giving content to investor sentiment: The role of media in the stock market. Journal of Finance, 62(3), 1139–1168.
11. Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3–28.
12. Basel Committee on Banking Supervision. (2019). Principles for effective risk data aggregation and risk reporting. Bank for International Settlements.
13. Campbell, J., Lo, A., & MacKinlay, A. (1997). The Econometrics of Financial Markets. Princeton University Press.
14. Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157–168.
15. Diebold, F. X. (2015). Comparing predictive accuracy, twenty years later. Journal of Business & Economic Statistics.
16. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.
17. Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably unequal? The effects of machine learning on credit markets. Journal of Finance, 77(1), 5–47.
18. Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd. Norton.
19. Jordan, M., & Mitchell, T. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260.
20. Silver, D. et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529.
21. Zhang, G., Patuwo, B., & Hu, M. (1998). Forecasting with artificial neural networks. International Journal of Forecasting, 14(1), 35–62.
22. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
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