Comparative Analysis of Financial Performance Among Traditional Quantitative, AI-Based, and Hybrid Modeling Approaches

Abstract

This work examines the evolution and integration of traditional quantitative models with artificial intelligence (AI) and machine learning (ML) techniques in financial forecasting. It highlights the strengths and limitations of classical statistical approaches such as ARIMA and GARCH, emphasizing their interpretability and stability under consistent market conditions, while acknowledging their challenges in capturing nonlinear dynamics and adapting to structural shifts. The study explores advanced AI architectures, including LSTM networks, CNNs, and attention mechanisms, which offer enhanced capabilities for modeling complex temporal and spatial patterns in heterogeneous financial data. Hybrid modeling strategies that combine statistical rigor with AI adaptability are analyzed, demonstrating improvements in predictive accuracy, noise reduction, and risk-adjusted performance across various asset classes and market domains. The integration methodologies, advantages, and potential drawbacks of such hybrids are discussed, alongside comprehensive evaluation frameworks employing return-based, risk-adjusted, error, and directional accuracy metrics. Broader applications in stock markets, foreign exchange, cryptocurrencies, derivatives, and diverse trading strategies are reviewed, with attention to uncertainty quantification and its impact on model reliability. The importance of explainable AI techniques and transparency in model outputs is underscored to meet regulatory and operational requirements. Human-machine collaboration is presented as a means to combine computational power with expert judgment effectively. Finally, considerations of computational complexity, scalability, ethical implications including data privacy, fairness, and regulatory compliance are addressed, highlighting current trends and future challenges in developing financial forecasting systems that balance accuracy, interpretability, and operational feasibility.

Keywords: Financial Forecasting, Hybrid Models, Deep Learning, Algorithmic Trading, Quantitative Finance, Sentiment Analysis, Model Interpretability, Risk-Adjusted Performance, Time-Series Analysis, Explainable AI (XAI)