(untitled) (Texas Journal of Multidisciplinary Studies, 2026)
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.