Artificial Intelligence and Data Analytics–Driven Financial Systems for Predicting Market Trends, Risk Management, and Portfolio Optimization in Dynamic Global Markets
Keywords:
Artificial Intelligence; Financial Market Prediction; Risk Management; Portfolio Optimization; Deep Learning; Hybrid Ensemble; LSTM; Transformer; Reinforcement Learning; Natural Language Processing; Quantitative FinanceAbstract
The increasing complexity, volatility, and nonlinear behaviour of global financial markets have exposed the limitations of traditional statistical and econometric models in accurately forecasting market trends, quantifying risk, and optimizing portfolio performance. To address these challenges, this study proposes a comprehensive Artificial Intelligence (AI) and data analytics–driven financial market intelligence framework that integrates supervised deep learning, unsupervised learning, natural language processing (NLP), and reinforcement learning into a unified predictive and decision-making architecture. The proposed system is designed to process heterogeneous financial data streams, including high-frequency market data, macroeconomic indicators, fundamental financial metrics, and unstructured alternative data such as news sentiment and social media information. The framework is evaluated across multiple asset classes, including equities, fixed income, foreign exchange, and commodities, ensuring robustness across diverse market conditions.
The hybrid ensemble achieves a directional accuracy of 84.3%, a root mean square error (RMSE) of 1.38%, and a Sharpe ratio of 1.67, outperforming classical linear regression models by 31.1%, 71.4%, and 169.4%, respectively. In risk management, the framework achieves a Value-at-Risk (VaR) breach rate of 2.4% and a maximum drawdown of 8.6%, reflecting significant improvements over traditional approaches. Additionally, the reinforcement learning–based portfolio optimization module yields an annual return of 24.3% and a Calmar ratio of 1.38. Overall, the results demonstrate that the proposed integrated AI framework significantly enhances predictive accuracy, strengthens risk control, and improves portfolio performance, providing a robust and scalable solution for intelligent financial decision-making in dynamic markets.
