DEPLOYMENT AND PERFORMANCE EVALUATION OF HYBRID MACHINE LEARNING MODELS FOR STOCK PRICE FORECASTING AND RISK PREDICTION IN VOLATILE MARKETS

This study investigates how hybrid machine learning systems can be implemented and deployed to deliver reliable stock price forecasting and risk prediction in volatile, internationally integrated markets. Using a PRISMA protocol, we reviewed 120 peer-reviewed studies with deployment-relevant detail, harmonized their metrics, and synthesized evidence across two layers: predictive performance and MLOps operations. The analysis shows that hybrids consistently convert modest single-digit reductions in point error into materially better probabilistic calibration, with tighter Value-at-Risk and Expected Shortfall coverage that holds up under walk-forward evaluation and during high-volatility regimes. Design patterns that travel well from lab to production include combining a decomposable statistical baseline with a tabular learner and a sequence or attention model, then learning dynamic, regime-aware weights on rolling residuals. On the engineering side, studies that report model registries, CI or CD gates, canary or shadow rollouts, drift and exceedance monitoring, and rollback playbooks exhibit smaller backtest-to-live gaps and lower reversal rates, highlighting that disciplined data contracts and promotion controls function as performance multipliers rather than overhead. Measurement choices further amplify deployability, as realized-volatility and lightweight range-based estimators improve distributional sharpness at low computational cost, while portable microstructure features strengthen short-horizon direction without violating latency budgets. Overall, the evidence supports a practical blueprint that integrates feature stores, reproducible pipelines, dynamic hybridization, and risk-aware monitoring to produce forecasting and risk services that are auditable, explainable, and resilient under market stress, turning incremental accuracy into dependable tail behavior suitable for real-world deployment.